CN112862638A - Remote education system and method based on face recognition - Google Patents
Remote education system and method based on face recognition Download PDFInfo
- Publication number
- CN112862638A CN112862638A CN201911176632.2A CN201911176632A CN112862638A CN 112862638 A CN112862638 A CN 112862638A CN 201911176632 A CN201911176632 A CN 201911176632A CN 112862638 A CN112862638 A CN 112862638A
- Authority
- CN
- China
- Prior art keywords
- unit
- student
- face image
- exercise
- teaching
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000012545 processing Methods 0.000 claims abstract description 19
- 238000012544 monitoring process Methods 0.000 claims description 22
- 238000012360 testing method Methods 0.000 claims description 18
- 238000001914 filtration Methods 0.000 claims description 12
- 230000005540 biological transmission Effects 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 18
- 238000001514 detection method Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 238000012512 characterization method Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 210000000056 organ Anatomy 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 108010001267 Protein Subunits Proteins 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000001815 facial effect Effects 0.000 description 1
- 230000005021 gait Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000012634 optical imaging Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
-
- 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
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
-
- 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
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/08—Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Educational Administration (AREA)
- General Health & Medical Sciences (AREA)
- Educational Technology (AREA)
- Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Multimedia (AREA)
- Human Computer Interaction (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- General Business, Economics & Management (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The invention discloses a remote education system and a method based on face recognition, which relate to the technical field of internet education and comprise the following steps: a teaching end and a learning end; the teaching end provides the teacher with the teaching task processing; the student terminal is provided for the student to complete a learning task; the teaching end includes: the exercise database is used for automatically generating an exercise database suitable for student learning and teacher explanation according to the data input by the teacher, and the exercise database is used for the teacher explanation and the student learning; the exercise editing unit is used for providing the teaching end to edit the exercises in the exercise database; the problem answering unit is used for providing the teacher with explanation for the problem exercise and answering the question of the student; the students are supervised through face recognition, and the teaching quality is improved through feedback solution of exercises.
Description
Technical Field
The invention relates to the technical field of remote education, in particular to a remote education system and method based on face recognition.
Background
Distance education is an education form which adopts various media ways to carry out system teaching and communication connection, and is education for delivering courses to one or more students outside a campus. Modern distance education refers to education in which courses are delivered through audio, video (live or video), and computer technology, both real-time and non-real-time. Modern distance education is a new education mode generated along with the development of modern information technology. The development of computer technology, multimedia technology and communication technology, especially the rapid development of internet (internet), makes the means of distance education have a qualitative leap, becoming distance education under the high and new technical conditions. Modern distance education is mainly based on modern distance education means, and is compatible with traditional teaching forms such as professor, letter, self-study and the like, and a multi-media optimization and combination education mode.
The remote teaching has various different forms, letter teaching, television teaching and broadcast teaching belong to the remote teaching category, and the teaching modes have many advantages, such as the teaching resources can be fully utilized to enable more people to be educated; however, the remote teaching method has great limitation in teaching, especially lack of teaching interaction, and the fatal weakness limits the remote teaching to specific professions and occasions. The remote teaching herein is a novel remote teaching model that is formed by utilizing communications, networks, multimedia, computer devices and technologies to overcome the limitations of conventional remote teaching. The modern remote teaching needs the support of a complete teaching system, the system has three links or control environments, and an anchor room is the teaching environment of a teacher and is used for collecting teaching information; the remote classroom is the environment for students to study, and receives teaching information and feeds back student information; both messages are controlled by a computer network.
Biometric identification technology is classified as one of ten major technologies that revolutionized human society in the 21 st century. The biological characteristic recognition technology is the most convenient and safe identity recognition technology at present, and the biological characteristic recognition technology recognizes a person per se without a marker outside the person. The biological characteristic identification technology utilizes physiological characteristics and behavior characteristics of people to identify identities, and mainly comprises fingerprint identification, face identification, iris identification, gait identification and the like. Face recognition is a big hotspot in the current biometric identification field. Compared with the fingerprint identification technology widely applied at present, the fingerprint identification method has the remarkable advantages of intuition, convenience, non-contact property, friendliness, high user acceptance and the like.
Although the existing remote teaching system can completely realize the whole teaching process, the supervision of the completion of teaching and the answers of student feedback are still lack, so that the teaching quality is still inferior to that of face-to-face teaching.
Disclosure of Invention
In view of the above, the present invention aims to provide a remote education system and method based on face recognition, which supervise students through face recognition and improve teaching quality through feedback solution of exercises.
In order to achieve the purpose, the invention adopts the following technical scheme:
a remote education system based on face recognition, comprising: a teaching end and a learning end; the teaching end provides the teacher with the teaching task processing; the student terminal is provided for the student to complete a learning task; the teaching end includes: the exercise database is used for automatically generating an exercise database suitable for student learning and teacher explanation according to the data input by the teacher, and the exercise database is used for the teacher explanation and the student learning; the exercise editing unit is used for providing the teaching end to edit the exercises in the exercise database; the problem answering unit is used for providing the teacher with explanation for the problem exercise and answering the question of the student; the student terminal comprises: the identification monitoring unit is used for monitoring the personnel identity of the student terminal; the student learning unit is used for extracting the exercises in the exercise database and displaying the exercises to the student end, and the student learns and tests according to the exercises and obtains a test result; the error statistic unit is used for carrying out statistics on the test result of the student terminal and marking difficult exercises according to the statistic result; and the problem marking unit is connected with the student learning unit and is used for receiving the problem exercises marked by the students.
Further, the identification monitoring unit includes: the first face image acquisition subunit is arranged in front of the face and used for acquiring a face image; the second face image acquisition subunit is arranged in front of the face and at a position different from the first face image acquisition subunit and used for acquiring a face image; the face recognition processing unit is used for receiving and recognizing the face image acquired by the first face image acquisition subunit, acquiring a first candidate set, receiving and recognizing the face image acquired by the second face image acquisition subunit, acquiring a second candidate set, and selecting a candidate object with similarity meeting a preset rule from the first candidate set and the second candidate set as a final recognition result; wherein the face recognition processing unit is configured to: selecting candidate objects with the similarity greater than a first preset threshold value in the first candidate set and selecting candidate objects with the similarity greater than a second preset threshold value in the second candidate set as a third candidate set; calculating the similarity sum of the same candidate object in the third candidate set; and determining whether the maximum of the similarity sums is greater than a third predetermined threshold; and when the maximum value is larger than the third preset threshold value, selecting the candidate object corresponding to the maximum value as a final recognition result.
Further, the exercise database is further connected with an exercise generation unit for automatically generating exercises, including: the structure setting unit is used for establishing a corresponding exercise database structure according to teaching requirements; the interface generation unit is used for generating a corresponding user interface according to the exercise database structure; the input receiving unit is connected with the interface generating unit and is used for receiving the exercise data input by the teacher through the user interface; and the exercise data generating unit is used for storing the teaching contents into the corresponding position of the exercise database.
Further, the editing is inputting, modifying and deleting operations.
Further, the identification monitoring unit further includes: the infrared transmission filtering subunit is arranged in front of the first camera and used for filtering visible light and collecting a black-and-white face image; and the infrared cut-off filtering subunit is arranged in front of the second camera and is used for filtering infrared light and acquiring a colorful face image.
A method for distance education based on face recognition, characterized in that the method performs the following steps: the problem database in the teaching end automatically generates a problem database suitable for student learning and teacher explanation according to the data input by the teacher, and is used for teacher explanation and student learning; the exercise editing unit is used for providing the teaching end to edit the exercises in the exercise database; the problem answering unit is provided for teachers to explain the problem exercises and answer the questions of the students; the student terminal comprises an identification monitoring unit for monitoring the personnel identity of the student terminal; the student learning unit extracts the exercises in the exercise database and displays the exercises to the student end, and the student learns and tests according to the exercises and obtains a test result; the error statistic unit is used for carrying out statistics on the test result of the student end and marking difficult exercises according to the statistic result; and the problem marking unit is connected with the student learning unit and receives the problem exercises marked by the students.
Further, the identification monitoring unit includes: the first face image acquisition subunit is arranged in front of the face and used for acquiring a face image; the second face image acquisition subunit is arranged in front of the face and at a position different from the first face image acquisition subunit and used for acquiring a face image; the face recognition processing unit is used for receiving and recognizing the face image acquired by the first face image acquisition subunit, acquiring a first candidate set, receiving and recognizing the face image acquired by the second face image acquisition subunit, acquiring a second candidate set, and selecting a candidate object with similarity meeting a preset rule from the first candidate set and the second candidate set as a final recognition result; wherein the face recognition processing unit is configured to: selecting candidate objects with the similarity greater than a first preset threshold value in the first candidate set and selecting candidate objects with the similarity greater than a second preset threshold value in the second candidate set as a third candidate set; calculating the similarity sum of the same candidate object in the third candidate set; and determining whether the maximum of the similarity sums is greater than a third predetermined threshold; and when the maximum value is larger than the third preset threshold value, selecting the candidate object corresponding to the maximum value as a final recognition result.
Further, the exercise database is further connected with an exercise generation unit for automatically generating exercises, including: the structure setting unit is used for establishing a corresponding exercise database structure according to teaching requirements; the interface generation unit is used for generating a corresponding user interface according to the exercise database structure; the input receiving unit is connected with the interface generating unit and is used for receiving the exercise data input by the teacher through the user interface; and the exercise data generating unit is used for storing the teaching contents into the corresponding position of the exercise database.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, the identity recognition and the learning state monitoring of the logged-in personnel can be realized by carrying out the face recognition on the logged-in personnel at the student end; meanwhile, the invention realizes teaching feedback of remote teaching by realizing exercise feedback, and improves teaching quality.
Drawings
The invention is described in further detail below with reference to the following figures and detailed description:
fig. 1 is a schematic system structure diagram of a remote education system based on face recognition according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating a method of remote education based on face recognition according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
Please refer to fig. 1. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions under which the present invention can be implemented, so that the present invention has no technical significance, and any structural modification, ratio relationship change, or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
Example 1
A remote education system based on face recognition, comprising: a teaching end and a learning end; the teaching end provides the teacher with the teaching task processing; the student terminal is provided for the student to complete a learning task; the teaching end includes: the exercise database is used for automatically generating an exercise database suitable for student learning and teacher explanation according to the data input by the teacher, and the exercise database is used for the teacher explanation and the student learning; the exercise editing unit is used for providing the teaching end to edit the exercises in the exercise database; the problem answering unit is used for providing the teacher with explanation for the problem exercise and answering the question of the student; the student terminal comprises: the identification monitoring unit is used for monitoring the personnel identity of the student terminal; the student learning unit is used for extracting the exercises in the exercise database and displaying the exercises to the student end, and the student learns and tests according to the exercises and obtains a test result; the error statistic unit is used for carrying out statistics on the test result of the student terminal and marking difficult exercises according to the statistic result; and the problem marking unit is connected with the student learning unit and is used for receiving the problem exercises marked by the students.
Specifically, the remote education information quantity is large, and the teaching efficiency can be greatly improved. In the information society, a large amount of information and knowledge are expected to be inaccessible in a traditional teaching mode in limited time, and on-line education takes multimedia as a channel for spreading information, so that a large amount of information can be spread at the same time, and the teaching efficiency is greatly improved.
Example 2
On the basis of the above embodiment, the identification monitoring unit includes: the first face image acquisition subunit is arranged in front of the face and used for acquiring a face image; the second face image acquisition subunit is arranged in front of the face and at a position different from the first face image acquisition subunit and used for acquiring a face image; the face recognition processing unit is used for receiving and recognizing the face image acquired by the first face image acquisition subunit, acquiring a first candidate set, receiving and recognizing the face image acquired by the second face image acquisition subunit, acquiring a second candidate set, and selecting a candidate object with similarity meeting a preset rule from the first candidate set and the second candidate set as a final recognition result; wherein the face recognition processing unit is configured to: selecting candidate objects with the similarity greater than a first preset threshold value in the first candidate set and selecting candidate objects with the similarity greater than a second preset threshold value in the second candidate set as a third candidate set; calculating the similarity sum of the same candidate object in the third candidate set; and determining whether the maximum of the similarity sums is greater than a third predetermined threshold; and when the maximum value is larger than the third preset threshold value, selecting the candidate object corresponding to the maximum value as a final recognition result.
Specifically, the research of the face recognition system starts in the 60 s of the 20 th century, the development of the computer technology and the optical imaging technology is improved after the 80 s, and the research really enters the early application stage in the later 90 s, and mainly adopts the technology implementation of the U.S., germany and japan; the key to the success of the face recognition system is whether the face recognition system has a core algorithm with a sharp end or not, and the recognition result has practical recognition rate and recognition speed; the human face recognition system integrates various professional technologies such as artificial intelligence, machine recognition, machine learning, model theory, expert system and video image processing, and meanwhile, the theory and implementation of intermediate value processing need to be combined, so that the human face recognition system is the latest application of biological feature recognition, the core technology of the human face recognition system is implemented, and the conversion from weak artificial intelligence to strong artificial intelligence is shown.
Example 3
On the basis of the previous embodiment, the problem database is further connected with a problem generation unit for automatically generating problems, and the problem generation unit comprises: the structure setting unit is used for establishing a corresponding exercise database structure according to teaching requirements; the interface generation unit is used for generating a corresponding user interface according to the exercise database structure; the input receiving unit is connected with the interface generating unit and is used for receiving the exercise data input by the teacher through the user interface; and the exercise data generating unit is used for storing the teaching contents into the corresponding position of the exercise database.
Example 4
On the basis of the previous embodiment, the editing is an input, modification and deletion operation.
Example 5
On the basis of the above embodiment, the identification monitoring unit further includes: the infrared transmission filtering subunit is arranged in front of the first camera and used for filtering visible light and collecting a black-and-white face image; and the infrared cut-off filtering subunit is arranged in front of the second camera and is used for filtering infrared light and acquiring a colorful face image.
Specifically, face image acquisition: different face images can be collected through the camera lens, and for example, static images, dynamic images, different positions, different expressions and the like can be well collected. When the user is in the shooting range of the acquisition equipment, the acquisition equipment can automatically search and shoot the face image of the user.
Face detection: in practice, face detection is mainly used for preprocessing of face recognition, namely, the position and size of a face are accurately calibrated in an image. The face image contains abundant pattern features, such as histogram features, color features, template features, structural features, Haar features, and the like. The face detection is to extract the useful information and to use the features to realize the face detection.
The mainstream face detection method adopts an Adaboost learning algorithm based on the characteristics, wherein the Adaboost algorithm is a method for classification, and combines weak classification methods to form a new strong classification method.
In the process of face detection, an Adaboost algorithm is used for picking out some rectangular features (weak classifiers) which can represent the face most, the weak classifiers are constructed into a strong classifier according to a weighted voting mode, and then a plurality of strong classifiers obtained by training are connected in series to form a cascade-structured stacked classifier, so that the detection speed of the classifier is effectively improved.
Example 6
As shown in fig. 2, a remote education method based on face recognition is characterized in that the method performs the following steps: the problem database in the teaching end automatically generates a problem database suitable for student learning and teacher explanation according to the data input by the teacher, and is used for teacher explanation and student learning; the exercise editing unit is used for providing the teaching end to edit the exercises in the exercise database; the problem answering unit is provided for teachers to explain the problem exercises and answer the questions of the students; the student terminal comprises an identification monitoring unit for monitoring the personnel identity of the student terminal; the student learning unit extracts the exercises in the exercise database and displays the exercises to the student end, and the student learns and tests according to the exercises and obtains a test result; the error statistic unit is used for carrying out statistics on the test result of the student end and marking difficult exercises according to the statistic result; and the problem marking unit is connected with the student learning unit and receives the problem exercises marked by the students.
Example 7
On the basis of the above embodiment, the identification monitoring unit includes: the first face image acquisition subunit is arranged in front of the face and used for acquiring a face image; the second face image acquisition subunit is arranged in front of the face and at a position different from the first face image acquisition subunit and used for acquiring a face image; the face recognition processing unit is used for receiving and recognizing the face image acquired by the first face image acquisition subunit, acquiring a first candidate set, receiving and recognizing the face image acquired by the second face image acquisition subunit, acquiring a second candidate set, and selecting a candidate object with similarity meeting a preset rule from the first candidate set and the second candidate set as a final recognition result; wherein the face recognition processing unit is configured to: selecting candidate objects with the similarity greater than a first preset threshold value in the first candidate set and selecting candidate objects with the similarity greater than a second preset threshold value in the second candidate set as a third candidate set; calculating the similarity sum of the same candidate object in the third candidate set; and determining whether the maximum of the similarity sums is greater than a third predetermined threshold; and when the maximum value is larger than the third preset threshold value, selecting the candidate object corresponding to the maximum value as a final recognition result.
Specifically, extracting the facial image features: features that can be used by a face recognition system are generally classified into visual features, pixel statistical features, face image transform coefficient features, face image algebraic features, and the like. The face feature extraction is performed on some features of the face. Face feature extraction, also known as face characterization, is a process of feature modeling for a face. The methods for extracting human face features are classified into two main categories: one is a knowledge-based characterization method; the other is a characterization method based on algebraic features or statistical learning.
The knowledge-based characterization method mainly obtains feature data which is helpful for face classification according to shape description of face organs and distance characteristics between the face organs, and feature components of the feature data generally comprise Euclidean distance, curvature, angle and the like between feature points. The human face is composed of parts such as eyes, nose, mouth, and chin, and geometric description of the parts and their structural relationship can be used as important features for recognizing the human face, and these features are called geometric features. The knowledge-based face characterization mainly comprises a geometric feature-based method and a template matching method.
Example 8
On the basis of the previous embodiment, the problem database is further connected with a problem generation unit for automatically generating problems, and the problem generation unit comprises: the structure setting unit is used for establishing a corresponding exercise database structure according to teaching requirements; the interface generation unit is used for generating a corresponding user interface according to the exercise database structure; the input receiving unit is connected with the interface generating unit and is used for receiving the exercise data input by the teacher through the user interface; and the exercise data generating unit is used for storing the teaching contents into the corresponding position of the exercise database.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional units, and in practical applications, the functions may be distributed by different functional units according to needs, that is, the units or steps in the embodiments of the present invention are further decomposed or combined, for example, the units in the foregoing embodiment may be combined into one unit, or may be further decomposed into multiple sub-units, so as to complete all or part of the functions described above. The names of the units and steps involved in the embodiments of the present invention are only for distinguishing the units or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative elements, method steps, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the software elements, method steps, and corresponding programs may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (8)
1. A remote education system based on face recognition, comprising: a teaching end and a learning end; the teaching end provides the teacher with the teaching task processing; the student terminal is provided for the student to complete a learning task; it is characterized in that the teaching end comprises: the exercise database is used for automatically generating an exercise database suitable for student learning and teacher explanation according to the data input by the teacher, and the exercise database is used for the teacher explanation and the student learning; the exercise editing unit is used for providing the teaching end to edit the exercises in the exercise database; the problem answering unit is used for providing the teacher with explanation for the problem exercise and answering the question of the student; the student terminal comprises: the identification monitoring unit is used for monitoring the personnel identity of the student terminal; the student learning unit is used for extracting the exercises in the exercise database and displaying the exercises to the student end, and the student learns and tests according to the exercises and obtains a test result; the error statistic unit is used for carrying out statistics on the test result of the student terminal and marking difficult exercises according to the statistic result; and the problem marking unit is connected with the student learning unit and is used for receiving the problem exercises marked by the students.
2. The system of claim 1, wherein the identification monitoring unit comprises: the first face image acquisition subunit is arranged in front of the face and used for acquiring a face image; the second face image acquisition subunit is arranged in front of the face and at a position different from the first face image acquisition subunit and used for acquiring a face image; the face recognition processing unit is used for receiving and recognizing the face image acquired by the first face image acquisition subunit, acquiring a first candidate set, receiving and recognizing the face image acquired by the second face image acquisition subunit, acquiring a second candidate set, and selecting a candidate object with similarity meeting a preset rule from the first candidate set and the second candidate set as a final recognition result; wherein the face recognition processing unit is configured to: selecting candidate objects with the similarity greater than a first preset threshold value in the first candidate set and selecting candidate objects with the similarity greater than a second preset threshold value in the second candidate set as a third candidate set; calculating the similarity sum of the same candidate object in the third candidate set; and determining whether the maximum of the similarity sums is greater than a third predetermined threshold; and when the maximum value is larger than the third preset threshold value, selecting the candidate object corresponding to the maximum value as a final recognition result.
3. The system of claim 1, wherein the problem database is further coupled to a problem generation unit for automatically generating problems, comprising: the structure setting unit is used for establishing a corresponding exercise database structure according to teaching requirements; the interface generation unit is used for generating a corresponding user interface according to the exercise database structure; the input receiving unit is connected with the interface generating unit and is used for receiving the exercise data input by the teacher through the user interface; and the exercise data generating unit is used for storing the teaching contents into the corresponding position of the exercise database.
4. The system of claim 1, wherein the edit is an enter, modify, delete operation.
5. The system of claim 4, wherein the identification monitoring unit further comprises: the infrared transmission filtering subunit is arranged in front of the first camera and used for filtering visible light and collecting a black-and-white face image; and the infrared cut-off filtering subunit is arranged in front of the second camera and is used for filtering infrared light and acquiring a colorful face image.
6. A method of remote education based on face recognition based on the system according to one of claims 1 to 5, characterized in that it carries out the following steps: the problem database in the teaching end automatically generates a problem database suitable for student learning and teacher explanation according to the data input by the teacher, and is used for teacher explanation and student learning; the exercise editing unit is used for providing the teaching end to edit the exercises in the exercise database; the problem answering unit is provided for teachers to explain the problem exercises and answer the questions of the students; the student terminal comprises an identification monitoring unit for monitoring the personnel identity of the student terminal; the student learning unit extracts the exercises in the exercise database and displays the exercises to the student end, and the student learns and tests according to the exercises and obtains a test result; the error statistic unit is used for carrying out statistics on the test result of the student end and marking difficult exercises according to the statistic result; and the problem marking unit is connected with the student learning unit and receives the problem exercises marked by the students.
7. The method of claim 6, wherein identifying the monitoring unit comprises: the first face image acquisition subunit is arranged in front of the face and used for acquiring a face image; the second face image acquisition subunit is arranged in front of the face and at a position different from the first face image acquisition subunit and used for acquiring a face image; the face recognition processing unit is used for receiving and recognizing the face image acquired by the first face image acquisition subunit, acquiring a first candidate set, receiving and recognizing the face image acquired by the second face image acquisition subunit, acquiring a second candidate set, and selecting a candidate object with similarity meeting a preset rule from the first candidate set and the second candidate set as a final recognition result; wherein the face recognition processing unit is configured to: selecting candidate objects with the similarity greater than a first preset threshold value in the first candidate set and selecting candidate objects with the similarity greater than a second preset threshold value in the second candidate set as a third candidate set; calculating the similarity sum of the same candidate object in the third candidate set; and determining whether the maximum of the similarity sums is greater than a third predetermined threshold; and when the maximum value is larger than the third preset threshold value, selecting the candidate object corresponding to the maximum value as a final recognition result.
8. The method of claim 7, wherein the problem database is further coupled to a problem generation unit for automatically generating problems, comprising: the structure setting unit is used for establishing a corresponding exercise database structure according to teaching requirements; the interface generation unit is used for generating a corresponding user interface according to the exercise database structure; the input receiving unit is connected with the interface generating unit and is used for receiving the exercise data input by the teacher through the user interface; and the exercise data generating unit is used for storing the teaching contents into the corresponding position of the exercise database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911176632.2A CN112862638A (en) | 2019-11-26 | 2019-11-26 | Remote education system and method based on face recognition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911176632.2A CN112862638A (en) | 2019-11-26 | 2019-11-26 | Remote education system and method based on face recognition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112862638A true CN112862638A (en) | 2021-05-28 |
Family
ID=75985787
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911176632.2A Pending CN112862638A (en) | 2019-11-26 | 2019-11-26 | Remote education system and method based on face recognition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112862638A (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1744148A (en) * | 2004-08-31 | 2006-03-08 | 英业达股份有限公司 | Long-distance teaching system and its method |
| CN101639891A (en) * | 2008-07-28 | 2010-02-03 | 汉王科技股份有限公司 | Double-camera face identification device and method |
| CN105069342A (en) * | 2015-08-23 | 2015-11-18 | 华南理工大学 | Control method for educational resource database right based on face identification |
| CN105701885A (en) * | 2014-11-25 | 2016-06-22 | 上海天脉聚源文化传媒有限公司 | Face identification access control system and implementation method thereof |
| CN105894262A (en) * | 2016-04-01 | 2016-08-24 | 袁艳荣 | Customer authentication system based on image identification |
| CN106407906A (en) * | 2016-08-31 | 2017-02-15 | 彭青 | Human face identification method |
| CN107122717A (en) * | 2017-03-31 | 2017-09-01 | 华北科技学院 | A kind of anti-cheating information control device of recognition of face |
| CN107563280A (en) * | 2017-07-24 | 2018-01-09 | 南京道熵信息技术有限公司 | Face identification method and device based on multi-model |
| CN107665328A (en) * | 2016-08-31 | 2018-02-06 | 彭青 | Face face recognition method |
| CN107967458A (en) * | 2017-12-06 | 2018-04-27 | 宁波亿拍客网络科技有限公司 | A kind of face identification method |
| CN109753904A (en) * | 2018-12-25 | 2019-05-14 | 仁福宜康(天津)科技有限公司 | A kind of face identification method and system |
-
2019
- 2019-11-26 CN CN201911176632.2A patent/CN112862638A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1744148A (en) * | 2004-08-31 | 2006-03-08 | 英业达股份有限公司 | Long-distance teaching system and its method |
| CN101639891A (en) * | 2008-07-28 | 2010-02-03 | 汉王科技股份有限公司 | Double-camera face identification device and method |
| CN105701885A (en) * | 2014-11-25 | 2016-06-22 | 上海天脉聚源文化传媒有限公司 | Face identification access control system and implementation method thereof |
| CN105069342A (en) * | 2015-08-23 | 2015-11-18 | 华南理工大学 | Control method for educational resource database right based on face identification |
| CN105894262A (en) * | 2016-04-01 | 2016-08-24 | 袁艳荣 | Customer authentication system based on image identification |
| CN106407906A (en) * | 2016-08-31 | 2017-02-15 | 彭青 | Human face identification method |
| CN107665328A (en) * | 2016-08-31 | 2018-02-06 | 彭青 | Face face recognition method |
| CN107122717A (en) * | 2017-03-31 | 2017-09-01 | 华北科技学院 | A kind of anti-cheating information control device of recognition of face |
| CN107563280A (en) * | 2017-07-24 | 2018-01-09 | 南京道熵信息技术有限公司 | Face identification method and device based on multi-model |
| CN107967458A (en) * | 2017-12-06 | 2018-04-27 | 宁波亿拍客网络科技有限公司 | A kind of face identification method |
| CN109753904A (en) * | 2018-12-25 | 2019-05-14 | 仁福宜康(天津)科技有限公司 | A kind of face identification method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110889672B (en) | Student card punching and class taking state detection system based on deep learning | |
| CN108304793B (en) | Online learning analysis system and method | |
| CN111027865B (en) | Teaching analysis and quality assessment system and method based on behavior and expression recognition | |
| CN109359215A (en) | Video intelligent method for pushing and system | |
| CN108399376A (en) | Student classroom learning interest intelligent analysis method and system | |
| WO2021077382A1 (en) | Method and apparatus for determining learning state, and intelligent robot | |
| CN111242049A (en) | Student online class learning state evaluation method and system based on facial recognition | |
| CN110543811B (en) | Deep learning-based non-cooperative examination personnel management method and system | |
| CN110163567A (en) | Classroom roll calling system based on multitask concatenated convolutional neural network | |
| CN109064389A (en) | A kind of free hand line draws the deep learning method of generation presence image | |
| CN109598226B (en) | Online examination cheating judgment method based on Kinect color and depth information | |
| Chakraborty et al. | Automatic student attendance system using face recognition | |
| CN111553218A (en) | Intelligent medical skill teaching monitoring system based on human body posture recognition | |
| CN113705510A (en) | Target identification tracking method, device, equipment and storage medium | |
| CN116109455B (en) | Language teaching auxiliary system based on artificial intelligence | |
| CN111353439A (en) | Method, device, system and equipment for analyzing teaching behaviors | |
| CN114783043B (en) | Child behavior track positioning method and system | |
| Praveena et al. | Implementation of smart attendance monitoring using open-CV and python | |
| CN111666829A (en) | Multi-scene multi-subject identity behavior emotion recognition analysis method and intelligent supervision system | |
| CN111325853A (en) | Remote tutoring system and method based on augmented reality glasses | |
| CN114187640A (en) | Learning situation observation method, system, equipment and medium based on online classroom | |
| CN207851897U (en) | The tutoring system of artificial intelligence based on TensorFlow | |
| CN112766130A (en) | Classroom teaching quality monitoring method, system, terminal and storage medium | |
| Yi et al. | Real time learning evaluation based on gaze tracking | |
| CN112862638A (en) | Remote education system and method based on face recognition |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210528 |
|
| RJ01 | Rejection of invention patent application after publication |