CN116798099A - Intelligent identification and management method and system for identities of labor workers - Google Patents
Intelligent identification and management method and system for identities of labor workers Download PDFInfo
- Publication number
- CN116798099A CN116798099A CN202310837360.6A CN202310837360A CN116798099A CN 116798099 A CN116798099 A CN 116798099A CN 202310837360 A CN202310837360 A CN 202310837360A CN 116798099 A CN116798099 A CN 116798099A
- Authority
- CN
- China
- Prior art keywords
- image
- face image
- splitting
- face
- partial images
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000007726 management method Methods 0.000 title claims description 56
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000012216 screening Methods 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims description 37
- 238000003860 storage Methods 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 14
- 230000008929 regeneration Effects 0.000 claims description 13
- 238000011069 regeneration method Methods 0.000 claims description 13
- 230000000694 effects Effects 0.000 abstract description 3
- 210000001331 nose Anatomy 0.000 description 10
- 238000009826 distribution Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 3
- 238000004080 punching Methods 0.000 description 3
- 238000005336 cracking Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 210000000887 face Anatomy 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
- G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
- H04N1/32101—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
- H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
- H04N1/32149—Methods relating to embedding, encoding, decoding, detection or retrieval operations
- H04N1/32267—Methods relating to embedding, encoding, decoding, detection or retrieval operations combined with processing of the image
- H04N1/32272—Encryption or ciphering
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/44—Secrecy systems
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/44—Secrecy systems
- H04N1/4406—Restricting access, e.g. according to user identity
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Health & Medical Sciences (AREA)
- Oral & Maxillofacial Surgery (AREA)
- General Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Signal Processing (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Collating Specific Patterns (AREA)
Abstract
The application relates to a method and a system for intelligent identification and management of the identity of a labor worker, wherein the method comprises the steps that when face image acquisition equipment acquires the face image of the labor worker, the face image is sent to an image splitting model; the image splitting model determines a splitting area based on the face image and splits the face image into a plurality of partial images based on the splitting area; identifying image features of a plurality of partial images, screening out target partial images containing the target features of the sensitive parts of the human face, and anonymizing the target partial images; and storing the anonymized face image and a plurality of partial images corresponding to the face image. The application has the effect of improving the safety of the face data of the labor staff.
Description
Technical Field
The application relates to the technical field of intelligent analysis, in particular to an intelligent identification and management method and system for identities of labor staff.
Background
Currently, in order to monitor the daily work of the staff, after the staff is dispatched to a designated unit, the staff is checked by face recognition, and the staff refers to the work index of the staff by checking the work data during the dispatching work.
In the aspect of face identification attendance, the input of the face data of the working staff relates to personal privacy, and the face data of the working staff is easy to leak under different network environments because the working staff can be dispatched to different units and places to work, and the risk of illegal utilization of the face data occurs, so that the data security protection of the face data of the working staff still needs to be further improved in the aspect of intelligent face identification of the working staff.
Disclosure of Invention
In order to improve the safety of the face data of the labor staff; the application provides a method and a system for intelligent identification and management of the identity of a labor worker.
The first object of the present application is achieved by the following technical solutions:
a labor personnel identity intelligent identification and management method comprises the following steps:
when face image acquisition equipment acquires a face image of a labor worker, the face image is sent to an image splitting model;
the image splitting model determines a splitting area based on the face image and splits the face image into a plurality of partial images based on the splitting area;
identifying image features of a plurality of partial images, and screening out target partial images which contain target features of the sensitive parts of the human face and belong to the target features;
anonymizing the target partial image;
and storing the anonymized face image and a plurality of partial images corresponding to the face image.
By adopting the technical scheme, after the labor staff is confirmed to be dispatched to a new working place, the face image acquisition equipment of the working place needs to acquire the face image of the labor staff so as to facilitate face comparison during follow-up daily card punching.
After further collecting the face image of the labor staff, splitting the face image into a plurality of partial images of the face, carrying out anonymization processing on the screened target partial images belonging to sensitive parts of the face, for example, blurring, covering and the like on the partial images of eyes, mouth and nose and the like, and finally storing the anonymized face image and a plurality of partial images corresponding to the face image; the human face image which is complete and clear for the labor staff is difficult to obtain by an illegal person, so that the protection of the privacy of the human face image of the labor staff is improved; and only partial sensitive face images are selected for anonymization, so that the anonymization range is small, and the accuracy of recognition can be maintained.
The present application is in a preferred example: the step of anonymizing the target partial image comprises the following steps:
identifying the region information of the target feature in the target partial image;
selecting a preset fuzzy algorithm and based on a preset fuzzy radius;
and carrying out blurring processing on the image of the region where the target feature is located based on the region information to obtain a blurred local image.
By adopting the technical scheme, the target features, namely the partial image areas needing anonymization processing in the target partial image, such as the outline areas of eyes, noses and mouths, are blurred to prevent illegal use, so that the local sensitive position of the face image is blurred to achieve the purpose of protecting the face image of the labor staff, and the areas which do not belong to the target features are reserved, such as the face shape and the color development part, so that the accuracy of face recognition is improved.
The present application is in a preferred example: the image splitting model determines a splitting area based on a face image, and splits the face image into a plurality of partial images based on the splitting area, and the method comprises the following steps:
when the image splitting model receives a face image, determining a plurality of splitting coordinate points in the face image based on target features;
based on the determined split coordinate points, connecting the split points according to preset tracks associated with the split coordinate points to determine split areas, wherein the split areas formed by face images acquired at different angles are different;
and splitting the face image into a plurality of partial images based on the splitting area.
By adopting the technical scheme, as the labor staff faces towards the face image acquisition equipment at different angles during punching, face images at different angles are required to be acquired so as to facilitate the face recognition accuracy of subsequent labor staff, further, the splitting of partial images of the face images at different angles is flexible, the target features in the face images at different angles are found, and further, the splitting track of the relevant splitting points is found, for example, the target features of two eyes of a user and the complete mouth features are found when the face is positive, but when the user is a side face in the face image, only the target features of one eye and the mouth contour are different, so that the face images are split by adopting different splitting tracks, and further, the target partial images and the partial images without anonymization processing can be accurately judged.
The present application is in a preferred example: after the step of storing the anonymized face image and the partial images corresponding to the face image, the following steps are executed:
when an acquisition instruction for accessing or downloading the face image is received, tracking an access path of the acquisition instruction;
when the access path is the face image after the direct access anonymization processing, the accessed face image is directly deleted;
when the access path is used for accessing a plurality of local images corresponding to the face image, a preset judging model judges whether the access sequence of the plurality of local images is consistent with the preset access sequence in real time, and the plurality of local images are subjected to pre-encryption processing;
if the access sequence of the local images is inconsistent with the preset access sequence, immediately deleting the face image and a plurality of local images corresponding to the face image;
and sending the illegal access message to a management terminal for managing the face image of the working personnel.
By adopting the technical scheme, only the image of the illegal personnel who still can appear in the blurring processing of the face image is downloaded to the labor personnel, in order to prevent the illegal personnel from illegally accessing and downloading the face image after the anonymization processing of the labor personnel from being used for other purposes, after the acquisition instruction is received, the access path of the acquisition instruction is tracked, and as the face image and a plurality of local images of the face image are uniformly stored and are different in storage positions, whether the user directly accesses the file position of the face image is judged through the access path, if so, the face image is immediately deleted and a management terminal for managing the face image of the labor personnel is notified.
If the access path is the position of a plurality of partial images corresponding to the face image, the access sequence of the partial images is identified through the judgment model to judge whether the visitor is a manager with access right, if the visitor is not loaded by encryption according to the preset access sequence of the partial images in the face image, the partial images corresponding to the face image are deleted directly, the repeated cracking opportunity of the visitor is greatly reduced, the management terminal can receive the information of irregular access and downloading in time, and the management terminal can check and upload again in time and acquire the face image of the labor staff with deleted face data.
The present application is in a preferred example: when the access path is for accessing a plurality of partial images corresponding to the face image, the preset judging model judges whether the access sequence of the partial images is consistent with the preset access sequence in real time, and the step of pre-encrypting the partial images comprises the following steps:
when the access path is a plurality of partial images corresponding to the access face image, the judgment model identifies the sequence number which is associated in advance with the first partial image obtained by decryption;
judging whether the sequence number of the decrypted partial image is consistent with the first preset number of the preset access sequence;
if so, judging whether the sequence number of the next decrypted partial image is consistent with the next preset number of the preset access sequence; until all the preset numbers are judged.
By adopting the technical scheme, through carrying out sequential numbering on a plurality of partial images in the same face image, such as arbitrary arrangement and combination of numbers 1 to 9, the preset number of the preset access sequence is a password-like digital character string, when a plurality of partial images in the same face image are accessed, the ordered positions of the partial images in the storage folder are required to be identified according to the sequential number, the partial images in the corresponding ordered positions in the access folder are decrypted one by one according to the preset number until all the partial images are accessed, the access legitimacy of the model is judged, if the sequence of accessing the partial images is inconsistent with the preset number, all the partial images of the face image are automatically deleted, a management terminal is informed to carry out risk investigation and re-entry acquisition of the face data, safety is improved in the aspect of protecting the privacy of the face image of a labor staff, and isolation of repeated access of an illegal person is realized.
The present application is in a preferred example: when the access path is the face image after the direct access anonymization processing, after the step of deleting the accessed face image directly, executing the following steps:
when a regeneration instruction of a face image of a management terminal is received, recognizing a plurality of partial images corresponding to the face image to be generated selected by the management terminal from the regeneration instruction;
and acquiring the sequence numbers of each of the partial images, inputting the partial images into a judgment model according to the sequence based on the preset numbers, and generating the corresponding anonymized face image by the judgment model.
By adopting the technical scheme, if an illegal person only accesses the face image or a legal person directly accesses the face image by misoperation, after the face image is deleted, the deleted face image can be immediately output by the judgment model after all partial images of the deleted face image are accessed in a correct sequence, so that the quick recovery of the face image is realized, and the recovery of the face image is more convenient.
The second object of the present application is achieved by the following technical solutions:
an intelligent identification and management system for the identity of a labor worker comprises:
the image acquisition module is used for transmitting the face image to the image splitting model when the face image acquisition equipment acquires the face image of the labor staff;
the image splitting module is used for determining a splitting area based on the face image by the image splitting model and splitting the face image into a plurality of partial images based on the splitting area;
the local image screening module is used for identifying image features of a plurality of local images and screening out target local images containing target features of the sensitive parts of the human face;
the image processing module is used for anonymizing the target local image;
and the storage module is used for storing the anonymized face image and a plurality of partial images corresponding to the face image.
By adopting the technical scheme, after the labor staff is confirmed to be dispatched to a new working place, the face image acquisition equipment of the working place needs to acquire the face image of the labor staff so as to facilitate face comparison during follow-up daily card punching.
After further collecting the face image of the labor staff, splitting the face image into a plurality of partial images of the face, carrying out anonymization processing on the screened target partial images belonging to sensitive parts of the face, for example, blurring, covering and the like on the partial images of eyes, mouth and nose and the like, and finally storing the anonymized face image and a plurality of partial images corresponding to the face image; the human face image which is complete and clear for the labor staff is difficult to obtain by an illegal person, so that the protection of the privacy of the human face image of the labor staff is improved; and only partial images are selected for anonymization, so that the anonymization range is small, and the accuracy of recognition can be maintained.
Optionally, the image processing module includes:
the region identification sub-module is used for identifying region information of the target features in the target partial image;
the algorithm selection submodule is used for selecting a preset fuzzy algorithm and is based on a preset fuzzy radius;
and the blurring processing sub-module is used for blurring the image of the area where the target feature is positioned based on the area information to obtain a blurred local image.
The third object of the present application is achieved by the following technical solutions:
the computer equipment comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the intelligent identification and management method for the identity of the labor staff when executing the computer program.
The fourth object of the present application is achieved by the following technical solutions:
a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the method for intelligent identification and management of the identity of a labor person.
In summary, the present application includes at least one of the following beneficial technical effects:
1. storing the anonymized face image and a plurality of partial images corresponding to the face image; the human face image which is complete and clear for the labor staff is difficult to obtain by an illegal person, so that the protection of the privacy of the human face image of the labor staff is improved; only partial images are selected for anonymization, so that the anonymization range is small, and the accuracy of recognition can be maintained;
2. the target features, namely the clear partial image areas, such as outline areas of eyes, noses and mouths, are reserved in the target partial image, the areas do not need to be blurred to improve the accuracy of face recognition, and the blurring process only blurs the areas which do not belong to the target features in the target partial image, so that the local sensitive positions of the face image are blurred, and the purpose of protecting the face image of the labor staff is achieved;
3. splitting the face image by adopting different splitting tracks, so that a target partial image and a partial image which does not need anonymization processing can be accurately judged;
4. if the illegal person does not carry out encryption loading according to the preset access sequence of a plurality of partial images in the face image, the partial images corresponding to the face image are directly deleted, the repeated cracking possibility of the illegal person is greatly reduced, the management terminal can timely receive the information of illegal access and downloading, and the management terminal can timely check and re-upload and collect the face image of the labor staff with the deleted face data.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for intelligent identification and management of the identity of a labor worker according to the present application;
FIG. 2 is a flowchart showing an implementation of step S40 in an embodiment of a method for intelligent identification and management of personnel identity according to the present application;
FIG. 3 is a flowchart showing an implementation of step S20 in an embodiment of a method for intelligent identification and management of personnel identity according to the present application;
FIG. 4 is a flowchart showing an implementation of the method for intelligent identification and management of the identity of a labor worker according to the embodiment of the present application after step S50;
FIG. 5 is a flowchart showing an implementation of the method for intelligent identification and management of the identity of a labor worker according to the embodiment of the present application after step S53;
fig. 6 is a schematic block diagram of a computer device of the present application.
Detailed Description
The application is described in further detail below with reference to fig. 1-6.
In one embodiment, as shown in fig. 1, the application discloses a method for intelligently identifying and managing the identity of a labor worker, which specifically comprises the following steps:
s10: when face image acquisition equipment acquires a face image of a labor worker, the face image is sent to an image splitting model;
in this embodiment, the face image acquisition device is an intelligent face recognition device with a camera and a communication function, and the face image acquisition device is used for being installed on a working place of a labor staff to be used for the labor staff to go to and off duty to punch cards.
The face image is a face image of the staff member acquired by the intelligent face recognition device, and is used for being prestored in the system to be used as a material for image comparison when the staff member performs face recognition each time. The splitting model is a trained model for splitting a face image into a plurality of face partial images according to preset requirements.
Specifically, when the intelligent face recognition device collects the complete face image of the labor staff, the collected face image of the single labor staff is sent to the split model.
S20: the image splitting model determines a splitting area based on the face image and splits the face image into a plurality of partial images based on the splitting area;
in this embodiment, the determination of the splitting area refers to determining each partial image area after splitting the face image, and also refers to splitting the track, and the obtained partial image is a partial face area of the face image.
Specifically, after the image splitting model receives a face image of a labor worker, determining a splitting track of the face image, namely, after determining a splitting area of the face image, splitting the face image into a plurality of partial images of the face image, wherein the partial images can be spliced into a complete face image.
S30: identifying image features of a plurality of partial images, and screening out target partial images which contain target features of the sensitive parts of the human face and belong to the target features;
in this embodiment, the target features are screened out by comparing the contours and features, and the target features, such as eyes, mouth, nose, and other faces, have high-recognition and sensitive face parts, and the target partial images refer to a plurality of partial images including eyes, mouth, nose in the face.
S40: anonymizing the target partial image;
in this embodiment, the anonymization processing of the image refers to removing, replacing or blurring processing on certain image data, so as to achieve the purpose of hiding or protecting the data, thereby protecting personal privacy information contained in the image. In the field of face images, anonymization processing generally involves removing sensitive information of face parts of a person, such as eyes, mouth, nose, and the like, and by retaining other features, such as face shapes, color development, and the like, better security and privacy protection effects are obtained on the anonymized image.
Specifically, the screened target partial images containing the characteristics of human face eyes, mouth and nose and the like are subjected to anonymization processing.
S50: and storing the anonymized face image and a plurality of partial images corresponding to the face image.
In this embodiment, the storage paths of the face image and a plurality of partial images corresponding to the face image are different, and a plurality of partial images of the same face image are stored in the same location, so that a labor worker can have a plurality of face images with different angles.
Specifically, the face image after anonymization processing of each staff member and a plurality of corresponding partial images are stored, and the storage paths of the face image and the corresponding partial images are different.
In one embodiment, referring to fig. 2, step S40 includes the steps of:
s401: identifying the region information of the target feature in the target partial image;
s402: selecting a preset fuzzy algorithm and based on a preset fuzzy radius;
s403: and carrying out blurring processing on the image of the region where the target feature is located based on the region information to obtain a blurred local image.
In this embodiment, the region information of the target feature is the region of the eyes, the mouth and the nose, and the common fuzzy algorithm includes gaussian fuzzy, mean fuzzy, median fuzzy and the like. Typically, this can be adjusted according to the resolution of the image and the degree of anonymity required.
The blurred local image refers to the target local image subjected to blurring processing. The partial image of the unblurred portion includes a face contour or color of the face image.
Specifically, the area where eyes, mouth and nose are located in the target partial image is identified, a Gaussian blur method is selected, the blur radius is determined, and the identified area is subjected to blur processing, so that a blurred target partial image is obtained.
In one embodiment, referring to fig. 3, step S20 includes the steps of:
s201: when the image splitting model receives a face image, determining a plurality of splitting coordinate points in the face image based on target features;
s202: based on the determined split coordinate points, connecting the split points according to preset tracks associated with the split coordinate points to determine split areas, wherein the split areas formed by face images acquired at different angles are different;
s203: and splitting the face image into a plurality of partial images based on the splitting area.
In this embodiment, the split coordinate point refers to a fitting center point of the region where the target feature is located, and after confirmation of the split coordinate point is completed. And splitting each target feature into different partial images according to the preset track associated with the splitting coordinate point, namely, each partial image only has one complete target feature.
Binding different preset tracks with the number and distribution positions of the split points, for example, the number and distribution positions of the split points when the side face of the labor staff is acquired are different from the number and distribution positions of the split points when the front face of the labor staff is acquired, so that the preset tracks are determined.
The face images acquired at different angles form different splitting areas, and the number of corresponding partial images may also be different. A human staff member stores face images of a plurality of different angles and a plurality of corresponding partial images simultaneously.
Specifically, when the image splitting model receives a single Zhang Ren face image of a labor worker, a splitting coordinate point of the face image is determined based on a face sensitive part in the face image, namely based on target features, the splitting coordinate point is a splitting identification point, a preset splitting track is confirmed through the number and the distribution positions of the splitting coordinate point, the face is split into a plurality of partial images, and each partial image only comprises one target feature.
In one embodiment, referring to fig. 4, after step S50, the following steps are performed:
s51: when an acquisition instruction for accessing or downloading the face image is received, tracking an access path of the acquisition instruction;
s52: when the access path is the face image after the direct access anonymization processing, the accessed face image is directly deleted;
s53: when the access path is used for accessing a plurality of local images corresponding to the face image, a preset judging model judges whether the access sequence of the plurality of local images is consistent with the preset access sequence in real time, and the plurality of local images are subjected to pre-encryption processing;
s54: if the access sequence of the local images is inconsistent with the preset access sequence, immediately deleting the face image and a plurality of local images corresponding to the face image;
s55: and sending the illegal access message to a management terminal for managing the face image of the working personnel.
In this embodiment, the storage positions of the face image and the corresponding partial images are different, so that it is necessary to determine and track the path of access.
Because the partial images of the same face image are all stored in the same position and have a certain arrangement sequence, the real-time judgment of whether the access sequence of the partial images is consistent with the preset access sequence by the judgment models means that the judgment models judge whether the access sequence of the partial images ordered by the accessor is consistent with the preset access sequence, for example, 6 partial images are all accessed for the first time, the second time, the 2 nd time, the third time, the fourth time, the 5 th time, the fifth time, the 1 st time and the sixth time. The visitor accesses the partial images in a fixed order in the aforementioned access order. If the access sequence is wrong, the face image and a plurality of partial images corresponding to the face image are deleted immediately.
The management terminal is generally used by a labor company administrator, and is used for managing the safety of face data information of labor staff, and the illegal access information refers to face images of the labor staff illegally accessed by users of the non-management terminal.
Specifically, when an acquisition instruction for accessing or downloading the face image is received, the access path of the acquisition instruction is tracked, and when the access path is direct access or the face image is to be downloaded, the accessed face image is immediately deleted directly.
When the access path is for accessing the local images corresponding to the face images, the judgment model judges whether the access sequence of the visitor to the arranged local images is consistent with the preset access sequence in real time, and if not, the face images and the local images corresponding to the face images are immediately deleted.
Further, if the access order is consistent with the preset access order, the visitor can download and access the face image normally.
In one embodiment, referring to fig. 5, step S53 includes the steps of:
s531: when the access path is a plurality of partial images corresponding to the access face image, the judgment model identifies the sequence number which is associated in advance with the first partial image obtained by decryption;
s532: judging whether the sequence number of the decrypted partial image is consistent with the first preset number of the preset access sequence;
s533: if so, judging whether the sequence number of the next decrypted partial image is consistent with the next preset number of the preset access sequence; until all the preset numbers are judged.
In the present embodiment, the sequence number depends on the ordering position of the partial image in the storage folder
Specifically, when the access path is a plurality of partial images corresponding to the access face image, the judgment model identifies the sequence number of the partial image obtained by decryption, namely identifies the sequence number of the partial image obtained by decryption in the partial image storage folders, judges whether the sequence number is consistent with the preset number which should be accessed by the first one in the preset access sequence, and if so, judges whether the sequence number of the partial image accessed by the visitor next is consistent with the preset number accessed by the second one.
And after the visitor accesses all the partial images and sequentially checks the sequence numbers of all the partial images, the judging model completes the judging process of the visitor accessing the partial images.
In one embodiment, after step S52, the following steps are further performed:
s521: when a regeneration instruction of a face image of a management terminal is received, recognizing a plurality of partial images corresponding to the face image to be generated selected by the management terminal from the regeneration instruction;
s522: and acquiring the sequence numbers of each of the partial images, inputting the partial images into a judgment model according to the sequence based on the preset numbers, and generating the corresponding anonymized face image by the judgment model.
In the present embodiment, the regeneration instruction is a control instruction for quickly generating the deleted face image after anonymization processing, which can be transmitted only by the management terminal.
Specifically, when the face image is deleted and is to be quickly restored, the management terminal sends a regeneration instruction, and when the regeneration instruction of the face image sent by the management terminal is received, a plurality of local images corresponding to the face image to be generated selected by the management terminal are identified from the regeneration instruction, and further, the management terminal should sequentially input the plurality of local images into the judgment model according to a preset access sequence, so that the face images corresponding to the local images can be survived.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
In an embodiment, an intelligent identification and management system for the identity of a labor staff is provided, and the intelligent identification and management method for the identity of the labor staff corresponds to the intelligent identification and management system for the identity of the labor staff in the embodiment. This labor personnel identity intelligent identification, management system includes:
the image acquisition module is used for transmitting the face image to the image splitting model when the face image acquisition equipment acquires the face image of the labor staff;
the image splitting module is used for determining a splitting area based on the face image by the image splitting model and splitting the face image into a plurality of partial images based on the splitting area;
the local image screening module is used for identifying image features of a plurality of local images and screening out target local images containing target features of the sensitive parts of the human face;
the image processing module is used for anonymizing the target local image;
and the storage module is used for storing the anonymized face image and a plurality of partial images corresponding to the face image.
Optionally, the image processing module includes:
the region identification sub-module is used for identifying region information of the target features in the target partial image;
the algorithm selection submodule is used for selecting a preset fuzzy algorithm and is based on a preset fuzzy radius;
and the blurring processing sub-module is used for blurring the image of the area where the target feature is positioned based on the area information to obtain a blurred local image.
Optionally, the image splitting module includes:
the splitting point determining sub-module is used for determining a plurality of splitting coordinate points in the face image based on the target characteristics when the face image is received by the image splitting model;
the track association sub-module is used for connecting the plurality of splitting points according to the preset tracks associated with the plurality of splitting coordinate points based on the determined plurality of splitting coordinate points so as to determine splitting areas, and the splitting areas formed by face images acquired at different angles are different;
and the splitting module is used for splitting the face image into a plurality of partial images based on the splitting area.
Optionally, the method further comprises:
the path tracking module is used for tracking the access path of the acquisition instruction when the acquisition instruction for accessing or downloading the face image is received;
the first deleting module is used for deleting the accessed face image directly when the access path is the face image after the anonymization processing;
the sequence judging module is used for judging whether the access sequence of the partial images is consistent with the preset access sequence in real time by a preset judging model when the access path is for accessing the partial images corresponding to the face images, and the partial images are subjected to pre-encryption processing;
the second deleting module is used for immediately deleting the face image and a plurality of local images corresponding to the face image if the access sequence of the local images is inconsistent with the preset access sequence;
and the prompting module is used for sending the illegal access message to a management terminal for managing the face image of the labor staff.
Optionally, the sequence judging module includes:
the number identification sub-module is used for judging that the model identifies the sequence number which is pre-associated with the first decrypted local image when the access path is a plurality of local images corresponding to the access face image;
the serial number judging sub-module is used for judging whether the serial number of the decrypted partial image is consistent with the first preset serial number of the preset access sequence; if so, judging whether the sequence number of the next decrypted partial image is consistent with the next preset number of the preset access sequence; until all the preset numbers are judged.
Optionally, the method further comprises:
the image regeneration request module is used for identifying a plurality of partial images corresponding to the face image to be generated selected by the management terminal from the regeneration instruction when the regeneration instruction of the face image of the management terminal is received;
the image generation module is used for acquiring the sequence numbers of the partial images, inputting the partial images into the judgment model according to the sequence based on the preset numbers, and generating the corresponding anonymized face images by the judgment model.
The specific limitation of the intelligent identification and management system for the labor staff can be referred to the limitation of the intelligent identification and management method for the labor staff hereinabove, and the detailed description is omitted here. All or part of each module in the intelligent identification and management system for the labor staff can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing an image splitting model, a face image, a local image, a target local image, a fuzzy local image and a judging model. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a method for intelligent identification and management of the identity of the staff.
In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing a method for intelligent identification and management of the identity of a labor worker when executing the computer program;
in one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements a method for intelligent identification and management of the identity of a labor person.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. An intelligent identification and management method for the identity of a labor worker is characterized in that: the method comprises the following steps:
when face image acquisition equipment acquires a face image of a labor worker, the face image is sent to an image splitting model;
the image splitting model determines a splitting area based on the face image and splits the face image into a plurality of partial images based on the splitting area;
identifying image features of a plurality of partial images, and screening out target partial images which contain target features of the sensitive parts of the human face and belong to the target features;
anonymizing the target partial image;
and storing the anonymized face image and a plurality of partial images corresponding to the face image.
2. The method for intelligently identifying and managing the identity of the labor staff according to claim 1, wherein the step of anonymizing the target partial image comprises the steps of:
identifying the region information of the target feature in the target partial image;
selecting a preset fuzzy algorithm and based on a preset fuzzy radius;
and carrying out blurring processing on the image of the region where the target feature is located based on the region information to obtain a blurred local image.
3. The method for intelligently identifying and managing the identity of the labor staff according to claim 1, wherein the step of determining a splitting area by the image splitting model based on the face image and splitting the face image into a plurality of partial images based on the splitting area comprises the steps of:
when the image splitting model receives a face image, determining a plurality of splitting coordinate points in the face image based on target features;
based on the determined split coordinate points, connecting the split points according to preset tracks associated with the split coordinate points to determine split areas, wherein the split areas formed by face images acquired at different angles are different;
and splitting the face image into a plurality of partial images based on the splitting area.
4. The intelligent identification and management method for the identity of the staff member according to claim 1, wherein after the step of storing the anonymized face image and the partial images corresponding to the face image, the following steps are executed:
when an acquisition instruction for accessing or downloading the face image is received, tracking an access path of the acquisition instruction;
when the access path is the face image after the direct access anonymization processing, the accessed face image is directly deleted;
when the access path is used for accessing a plurality of local images corresponding to the face image, a preset judging model judges whether the access sequence of the plurality of local images is consistent with the preset access sequence in real time, and the plurality of local images are subjected to pre-encryption processing;
if the access sequence of the local images is inconsistent with the preset access sequence, immediately deleting the face image and a plurality of local images corresponding to the face image;
and sending the illegal access message to a management terminal for managing the face image of the working personnel.
5. The intelligent identification and management method for the identity of the staff member according to claim 4, wherein when the access path is a plurality of partial images corresponding to the access face image, the preset judgment model judges in real time whether the access sequence of the plurality of partial images is consistent with the preset access sequence, and the step of pre-encrypting the plurality of partial images comprises the following steps:
when the access path is a plurality of partial images corresponding to the access face image, the judgment model identifies the sequence number which is associated in advance with the first partial image obtained by decryption;
judging whether the sequence number of the decrypted partial image is consistent with the first preset number of the preset access sequence;
if so, judging whether the sequence number of the next decrypted partial image is consistent with the next preset number of the preset access sequence; until all the preset numbers are judged.
6. The intelligent identification and management method for the identity of a staff member according to claim 5, wherein when the access path is the face image after the anonymization processing is directly accessed, the following steps are executed after the step of directly deleting the accessed face image:
when a regeneration instruction of a face image of a management terminal is received, recognizing a plurality of partial images corresponding to the face image to be generated selected by the management terminal from the regeneration instruction;
and acquiring the sequence numbers of each of the partial images, inputting the partial images into a judgment model according to the sequence based on the preset numbers, and generating the corresponding anonymized face image by the judgment model.
7. An intelligent identification and management system for the identity of a labor worker is characterized by comprising:
the image acquisition module is used for transmitting the face image to the image splitting model when the face image acquisition equipment acquires the face image of the labor staff;
the image splitting module is used for determining a splitting area based on the face image by the image splitting model and splitting the face image into a plurality of partial images based on the splitting area;
the local image screening module is used for identifying image features of a plurality of local images and screening out target local images containing target features of the sensitive parts of the human face;
the image processing module is used for anonymizing the target local image;
and the storage module is used for storing the anonymized face image and a plurality of partial images corresponding to the face image.
8. The intelligent identification and management system for the identity of a labor person according to claim 7, wherein the image processing module comprises:
the region identification sub-module is used for identifying region information of the target features in the target partial image;
the algorithm selection submodule is used for selecting a preset fuzzy algorithm and is based on a preset fuzzy radius;
and the blurring processing sub-module is used for blurring the image of the area where the target feature is positioned based on the area information to obtain a blurred local image.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, carries out the steps of a method for intelligent identification and management of the identity of a staff member as claimed in any of the claims 1 to 6.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of a method for intelligent identification and management of the identity of a staff member according to any of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310837360.6A CN116798099B (en) | 2023-07-07 | 2023-07-07 | Intelligent identification and management method and system for identities of labor workers |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310837360.6A CN116798099B (en) | 2023-07-07 | 2023-07-07 | Intelligent identification and management method and system for identities of labor workers |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116798099A true CN116798099A (en) | 2023-09-22 |
| CN116798099B CN116798099B (en) | 2024-01-12 |
Family
ID=88040067
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310837360.6A Active CN116798099B (en) | 2023-07-07 | 2023-07-07 | Intelligent identification and management method and system for identities of labor workers |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116798099B (en) |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000067220A (en) * | 1998-08-21 | 2000-03-03 | Canon Inc | System, device and method for processing image |
| JP2014167674A (en) * | 2013-02-28 | 2014-09-11 | Ntt Data Corp | Database access verification method, database access verification device, and program |
| CN105208045A (en) * | 2015-10-28 | 2015-12-30 | 广东欧珀移动通信有限公司 | Identity authentication method, equipment and system |
| CN107295016A (en) * | 2017-08-10 | 2017-10-24 | 广州杰之良软件有限公司 | Auth method and device |
| US10108791B1 (en) * | 2015-03-19 | 2018-10-23 | Amazon Technologies, Inc. | Authentication and fraud detection based on user behavior |
| KR20190120663A (en) * | 2018-04-16 | 2019-10-24 | 주식회사 아이에이 | Image receiving device, image reconstructing method thereof, method for image masking and reconstructing of image system |
| CN111814145A (en) * | 2020-08-19 | 2020-10-23 | 深圳市富之富信息科技有限公司 | Request verification method and device based on log acquisition and computer equipment |
| CN111931145A (en) * | 2020-06-29 | 2020-11-13 | 北京爱芯科技有限公司 | Face encryption method, face recognition method, face encryption device, face recognition device, electronic equipment and storage medium |
| CN112188086A (en) * | 2020-09-09 | 2021-01-05 | 中国联合网络通信集团有限公司 | Image processing method and device |
| WO2021027955A1 (en) * | 2019-08-15 | 2021-02-18 | 纳恩博（常州）科技有限公司 | Information processing method and device, and storage medium |
| CN112766028A (en) * | 2019-11-06 | 2021-05-07 | 深圳云天励飞技术有限公司 | Face fuzzy processing method and device, electronic equipment and storage medium |
| CN114496175A (en) * | 2022-02-14 | 2022-05-13 | 联仁健康医疗大数据科技股份有限公司 | Medical image viewing method, device, equipment and storage medium |
| CN115588222A (en) * | 2022-10-09 | 2023-01-10 | 杭州指安科技股份有限公司 | Door lock-based data security face recognition system and method |
| CN115909449A (en) * | 2022-11-14 | 2023-04-04 | 北京淘友天下技术有限公司 | File processing method, file processing device, electronic equipment, storage medium and program product |
| CN116129496A (en) * | 2022-12-23 | 2023-05-16 | 浙江大华技术股份有限公司 | Image shielding method and device, computer equipment and storage medium |
-
2023
- 2023-07-07 CN CN202310837360.6A patent/CN116798099B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000067220A (en) * | 1998-08-21 | 2000-03-03 | Canon Inc | System, device and method for processing image |
| JP2014167674A (en) * | 2013-02-28 | 2014-09-11 | Ntt Data Corp | Database access verification method, database access verification device, and program |
| US10108791B1 (en) * | 2015-03-19 | 2018-10-23 | Amazon Technologies, Inc. | Authentication and fraud detection based on user behavior |
| CN105208045A (en) * | 2015-10-28 | 2015-12-30 | 广东欧珀移动通信有限公司 | Identity authentication method, equipment and system |
| CN107295016A (en) * | 2017-08-10 | 2017-10-24 | 广州杰之良软件有限公司 | Auth method and device |
| KR20190120663A (en) * | 2018-04-16 | 2019-10-24 | 주식회사 아이에이 | Image receiving device, image reconstructing method thereof, method for image masking and reconstructing of image system |
| WO2021027955A1 (en) * | 2019-08-15 | 2021-02-18 | 纳恩博（常州）科技有限公司 | Information processing method and device, and storage medium |
| CN112766028A (en) * | 2019-11-06 | 2021-05-07 | 深圳云天励飞技术有限公司 | Face fuzzy processing method and device, electronic equipment and storage medium |
| CN111931145A (en) * | 2020-06-29 | 2020-11-13 | 北京爱芯科技有限公司 | Face encryption method, face recognition method, face encryption device, face recognition device, electronic equipment and storage medium |
| CN111814145A (en) * | 2020-08-19 | 2020-10-23 | 深圳市富之富信息科技有限公司 | Request verification method and device based on log acquisition and computer equipment |
| CN112188086A (en) * | 2020-09-09 | 2021-01-05 | 中国联合网络通信集团有限公司 | Image processing method and device |
| CN114496175A (en) * | 2022-02-14 | 2022-05-13 | 联仁健康医疗大数据科技股份有限公司 | Medical image viewing method, device, equipment and storage medium |
| CN115588222A (en) * | 2022-10-09 | 2023-01-10 | 杭州指安科技股份有限公司 | Door lock-based data security face recognition system and method |
| CN115909449A (en) * | 2022-11-14 | 2023-04-04 | 北京淘友天下技术有限公司 | File processing method, file processing device, electronic equipment, storage medium and program product |
| CN116129496A (en) * | 2022-12-23 | 2023-05-16 | 浙江大华技术股份有限公司 | Image shielding method and device, computer equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 张滇: "基于GCC的中间代码优化技术研究", 《万方数据》 * |
| 戴华东等: "以操作***为中心的存储一致性模型--线程一致性模型", 《计算机研究与发展》, pages 1 - 9 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116798099B (en) | 2024-01-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109684347B (en) | Account checking method, account checking device, computer equipment and storage medium | |
| CN109410073B (en) | Image file desensitizing method, device, computer equipment and storage medium | |
| CN111835756A (en) | APP privacy compliance detection method and device, computer equipment and storage medium | |
| CN104335252A (en) | Method for identifying a person | |
| CN115292294A (en) | Database security management method and system | |
| CN109960936A (en) | A kind of pair of mobile terminal carries out the Risk Identification Method of automatization simulation business access | |
| CN111708971A (en) | Page configuration method and device, computer equipment and storage medium | |
| JP2012150617A (en) | Operation information gathering system, operation information gathering system control method, and its control program | |
| CN110674484A (en) | Monitoring system and method for intelligent stamping machine | |
| CN116798099B (en) | Intelligent identification and management method and system for identities of labor workers | |
| CN104486306A (en) | Method for identity authentication based on finger vein recognition and cloud service | |
| CN103678985A (en) | Automatic operator tag generating system and automatic operator tag generating method | |
| CN112910868A (en) | Enterprise network security management method and device, computer equipment and storage medium | |
| CN112433925A (en) | Log generation method and device based on artificial intelligence, computer equipment and medium | |
| CN109726187B (en) | Hadoop-oriented adaptive permission control method and device | |
| CN114417397A (en) | Behavior portrait construction method and device, storage medium and computer equipment | |
| CN114238911A (en) | Financial escort handing-over system | |
| CN113987506A (en) | Test method, device, test equipment and storage medium of authentication system | |
| CN112817816A (en) | Embedded point processing method and device, computer equipment and storage medium | |
| CN112185495A (en) | Medical equipment case data acquisition method and system | |
| CN111176630A (en) | Method and device for changing identity information of person on duty | |
| CN115426481B (en) | 5G and artificial intelligence based gridding big data city monitoring method and system | |
| CN118133261B (en) | Method, device and equipment for reading and writing card of business desktop flat plate and storage medium | |
| US20240185196A1 (en) | Reduced memory resources for proactive monitoring of electronic transactions | |
| CN117688541B (en) | Data processing system is expanded in electric power industry |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |