CN110889326B - Human body detection-based queue-jumping behavior monitoring and warning system, method, device and storage medium - Google Patents
Human body detection-based queue-jumping behavior monitoring and warning system, method, device and storage medium Download PDFInfo
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Abstract
The invention discloses a queue-insertion behavior monitoring and warning system, method and device based on human body detection and a storage medium, wherein the queue-insertion behavior monitoring and warning system comprises a queue detection unit, a queue-insertion behavior detection unit and a characteristic identification unit; the queue detection unit carries out basic modeling on the indoor scene and determines the range of the detection queue; the queue-insertion behavior detection unit positions and detects the human face and confirms the queue-insertion human face by using a blink recognition technology; the characteristic identification unit carries out face shooting with the help of the camera, carries out characteristic identification and broadcasts to the shot face, and finishes the warning of the queue-insertion behavior. The invention realizes the function of automatically detecting the non-civilized behavior of the queue-insertion with high precision, is beneficial to warning the queue-insertion behavior and promoting harmonious queuing.
Description
Technical Field
The invention relates to a face recognition technology, in particular to a queue-jumping behavior monitoring and warning system, method, device and storage medium based on human body detection.
Background
With the continuous and rapid development of modern scientific technology, a pattern learning training technology based on big data processing gradually appears in the visual field of people, and the technology is used in more and more fields regardless of repeated product tests or artificial intelligence detection.
At present, in a pattern learning training technology based on big data processing, face recognition has become one of the directions needing important research and development, and the application based on face recognition gradually moves to various markets, and identity verification is used from the initial evasion recognition to the high-speed rail trip at present. Face recognition offers many benefits to these scenarios, but the face recognition databases used in these cases are very large, require complex and sophisticated algorithms for technical support, and can be very costly to use in small tasks.
The face recognition can also be used for auxiliary education, such as queue-insertion behaviors, aiming at the uncivilized behaviors appearing in the modern society. In Chinese culture, people are still unconsciously endured and unknown in most cases even if people encounter the uneventful behaviors since ancient times. However, this behavior actually destroys social order, and further makes the person who is the party unconscious of his own misbehavior, which further makes him unsure and often makes no change.
The social diathesis is a component of the overall diathesis of a person, is the sum of the general fostering degree, progress degree, civilization degree, morality degree and mental state of the population forming the society, and reflects the development degree and modernization degree of the society. The frequent teaching of the queue-inserting behavior is not changed, and the improvement of the overall social quality is very unfavorable after the past. Therefore, if the manual inspection mode is adopted, the labor is too wasted, and the detection warning of the action needs to be carried out by using a face recognition mode. Through technical means, utilize intelligent system automated inspection to insert the action of team, further carry out pronunciation warning to the people of inserting the team just now, neither extravagant manpower like this, can play the effect of effective warning again.
Disclosure of Invention
The purpose of the invention is as follows: the utility model provides a monitoring and warning system of queue-in behavior based on human body detection, in order to solve the above-mentioned problem.
The technical scheme is as follows: a queue-insertion behavior monitoring and warning system based on human body detection comprises a queue detection unit, a queue-insertion behavior detection unit and a feature recognition unit, wherein data of the queue detection unit, the queue-insertion behavior detection unit and the feature recognition unit are stored, processed and transmitted by utilizing neural network training;
the queue detection unit is used for carrying out basic modeling on the scenes in the set range by using Gaussian filtering, accurately positioning the queue, regulating and controlling the measurement range in real time and updating the queue to be measured along with the change of the queue;
the queue-insertion behavior detection unit is used for carrying out blink recognition on eye frames of the human faces by using frame positioning, and positioning the detected human faces; when a non-sequential face is detected and a living body is confirmed, comparing the detected face with a stored face and confirming the detected face as a queue-insertion face, and positioning the queue-insertion face;
the characteristic identification unit is used for shooting a human face by means of a camera, identifying the characteristics of the shot human face and broadcasting the characteristic to finish the warning of the queue-insertion behavior;
the queue detection unit confirms a shooting area according to a camera installation angle, after the shooting area is input into a system, the shooting area is divided by a blue frame, a human face is positioned by using a computer vision field target detection technology, a blue solid frame is used for identifying the human face, a detected queue is confirmed by a black thin-line frame, the black thin-line frame is synchronously updated according to the change of the queuing queue range of the shooting area, and the method specifically comprises the following steps:
step 1, taking a first face identified by one side of a close-up camera head in a shooting area as a reference, taking the first face as an initial point position of a black thin line frame, and recording horizontal and vertical coordinates which are respectively 0;
step 2, taking the last face identified by one side of the telephoto image head in the shooting area as a reference, taking the last face as an end point of a black thin line frame, and recording a vertical coordinate;
step 3, performing image processing on the initial point position and the end point position by using a Gaussian filtering method, removing high-frequency information generated by a frame region due to large color change, and ensuring that all human faces in a shooting region are identified;
and 4, updating and detecting the queue of the shooting area at an interval of ten seconds, and repeating the step 1 to update the queue when an unidentified face appears at an initial point or an end point when the initial point or the end point is positioned according to a computer vision field target detection technology.
According to an aspect of the present invention, the queue detection unit reuses a gaussian filtering method to smooth the high frequency information of the edge region of the input image and remove noise, and the formula is:
G（x，y）=eE/2Πσ2；
Fs（x，y）=f（x，y）* G（x，y）；
wherein E = - (x)2+y2）/2σ2F (x, y) is data of an input image, and Fs (x, y) is image data after gaussian convolution.
According to one aspect of the invention, the edge detection of the input image can cause the phenomenon of inaccurate alignment of the queuing area due to the influence of false edges, a double threshold method is used for removing errors caused by alignment of the false edges, 0.7/0.6 and 0.15 are respectively selected as a highest threshold and a lowest threshold, and points higher than the highest threshold are set as 1, and points lower than the lowest threshold are set as 0.
According to one aspect of the invention, the queue-jumping behavior detection unit is used for dynamically acquiring the queuing queue frame in real time, when queue-jumping behavior occurs, the width of the black thin-line frame is changed by exceeding a threshold value and is usually larger than the width of a head frame of a person, and the angle of the camera can be further adjusted or a shot image of the upper body of the queue-jumping person can be acquired by detecting the change and preliminarily locking the person trying to queue-jump.
According to one aspect of the invention, the characteristic identification unit sends the queue-inserting person half-body frame obtained by the queue-inserting behavior detection technology into a neural network, identifies the clothes color and the gender of the queue-inserting person, converts the clothes color and the gender into text information and voice information, and broadcasts and reminds the clothes color and the gender.
A queue-insertion behavior monitoring and warning method based on human body detection comprises the following steps:
step 1, taking a first face identified by one side of a close-up camera head in a shooting area as a reference, taking the first face as an initial point position of a black thin line frame, and recording horizontal and vertical coordinates which are respectively 0;
step 2, taking the last face identified by one side of the telephoto image head in the shooting area as a reference, taking the last face as an end point of a black thin line frame, and recording a vertical coordinate;
step 3, performing image processing on the initial point position and the end point position by using a Gaussian filtering method, removing high-frequency information generated by a frame region due to large color change, and ensuring that all human faces in a shooting region are identified;
and 4, updating and detecting the queue of the shooting area at an interval of ten seconds, and repeating the step 1 to update the queue when an unidentified face appears at an initial point or an end point by positioning according to a computer vision field target detection technology.
The blink recognition face positioning method specifically comprises the following steps:
in order to avoid the influence of the queue width caused by the queue misalignment, the face recognition error removal is needed, that is, the face in the queue is recognized and recorded, and when the queue width exceeds the threshold value, the error caused by the queue misalignment is removed in advance, and the method specifically comprises the following steps:
step 1, positioning face characteristic points on a picture of a detection frame by framing the picture of a shot video so as to obtain eye key points;
step 2, constructing a human eye width-height ratio model, carrying out blink detection, locking a human face when the blink frame detection probability in thirty seconds reaches 1/10-1/3, eliminating the face influence of an inactive person, and recording face data;
and 3, when the queue width exceeds the threshold value, framing the currently shot queue picture, comparing the currently shot queue picture with the result of face recognition five times before the moment after face recognition is carried out, if the number and the result of the face recognition are not changed, determining that the width is changed due to the fact that the queue is not uniform, otherwise, carrying out face hole search without recognition, and positioning the faces of the queue inserts.
According to one aspect of the invention, the face recognition uses a 68-point feature point positioning algorithm based on a cascade regression tree, for a fixed frame picture to be detected, the algorithm generates an initial shape and initial position coordinates of 68 feature points, the sum of square errors between the initial shape and a true value is further reduced by using a gradient lifting algorithm, and the accuracy of face recognition and eye position positioning is ensured.
According to one aspect of the present invention, the face recognition record value is set for a special condition, and for a queue change caused by incomplete queuing due to direct departure of people from the queue, the face recognition record value needs to be set as a normal condition, and status exclusion is performed, specifically including the steps of:
step 1, using face recognition to identify faces in a queue, and marking faces after positioning eyes;
step 2, after face loss occurs, team face elimination recognition is carried out, team face updating is carried out synchronously, inter-team early warning is not started, when the fact that the missing face cannot be recognized in the team is confirmed, the fact that people in a queuing queue leave uncompleted queuing tasks is considered to occur, face relocation and recognition are carried out on the queuing queue;
and 3, confirming that the queue width changes and restores to normal along with the disappearance of the missing face, and restoring normal face recognition and blink recognition.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
step 1, taking a first face identified by one side of a close-up camera head in a shooting area as a reference, taking the first face as an initial point position of a black thin line frame, and recording horizontal and vertical coordinates which are respectively 0;
step 2, taking the last face identified by one side of the telephoto image head in the shooting area as a reference, taking the last face as an end point of a black thin line frame, and recording a vertical coordinate;
step 3, performing image processing on the initial point position and the end point position by using a Gaussian filtering method, removing high-frequency information generated by a frame region due to large color change, and ensuring that all human faces in a shooting region are identified;
and 4, updating and detecting the queue of the shooting area at an interval of ten seconds, and repeating the step 1 to update the queue when an unidentified face appears at an initial point or an end point when the initial point or the end point is positioned according to a computer vision field target detection technology.
10. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of:
step 1, taking a first face identified by one side of a close-up camera head in a shooting area as a reference, taking the first face as an initial point position of a black thin line frame, and recording horizontal and vertical coordinates which are respectively 0;
step 2, taking the last face identified by one side of the telephoto image head in the shooting area as a reference, taking the last face as an end point of a black thin line frame, and recording a vertical coordinate;
step 3, performing image processing on the initial point position and the end point position by using a Gaussian filtering method, removing high-frequency information generated by a frame region due to large color change, and ensuring that all human faces in a shooting region are identified;
and 4, updating and detecting the queue of the shooting area at an interval of ten seconds, and repeating the step 1 to update the queue when an unidentified face appears at an initial point or an end point when the initial point or the end point is positioned according to a computer vision field target detection technology.
Has the advantages that: the invention can realize the automatic face recognition and human body positioning of the queue insertion behavior, and determines the range of the queue through a Gaussian filtering method and a false edge removing method; further determining queuing human faces in the queue by using a human face recognition and blink detection method; through special circumstances setting, get rid of because of lining up irregularly, the task of lining up incomplete directly leave behind the width error that team caused, can accurately carry out the face identification of inserting a team and fix a position, further carry out the face of inserting a team and shoot, make things convenient for the camera to shoot and further characteristic information draws, carry out voice warning to the people of inserting a team.
Drawings
FIG. 1 is a system control schematic of the present invention.
Fig. 2 is a schematic diagram of queue detection of the queue detection unit of the present invention.
Fig. 3 is a schematic diagram of the queue-break behavior detected by the queue-break behavior detection unit of the present invention.
Detailed Description
As shown in fig. 1, in this embodiment, an inter-queue behavior monitoring and warning system based on human body detection includes a queue detection unit, an inter-queue behavior detection unit, and a feature recognition unit, and stores, processes, and transmits data of these three units by using neural network training;
the queue detection unit is used for carrying out basic modeling on the scenes in the set range by using Gaussian filtering, accurately positioning the queue, regulating and controlling the measurement range in real time and updating the queue to be measured along with the change of the queue;
the queue-insertion behavior detection unit is used for carrying out blink recognition on eye frames of the human faces by using frame positioning, and positioning the detected human faces; when a non-sequential face is detected and a living body is confirmed, comparing the detected face with a stored face and confirming the detected face as a queue-insertion face, and positioning the queue-insertion face;
the characteristic identification unit is used for shooting a human face by means of a camera, identifying the characteristics of the shot human face and broadcasting the characteristic to finish the warning of the queue-insertion behavior;
the queue detection unit, as shown in fig. two, confirms a shooting area according to a camera installation angle, after inputting a system, divides a queuing area by a blue frame, positions a human face by using a computer vision field target detection technology, identifies the human face by using a blue solid frame, confirms a detected queue by using a black thin wire frame, and synchronously updates the black thin wire frame according to the change of the queuing queue range of the shooting area, and specifically comprises the following steps:
step 1, taking a first face identified by one side of a close-up camera head in a shooting area as a reference, taking the first face as an initial point position of a black thin line frame, and recording horizontal and vertical coordinates which are respectively 0;
step 2, taking the last face identified by one side of the telephoto head of the shooting area as a reference, taking the last face as an end point of a black thin line frame, and recording a vertical coordinate;
step 3, performing image processing on the initial point position and the end point position by using a Gaussian filtering method, removing high-frequency information generated by a frame region due to large color change, and ensuring that all human faces in a shooting region are identified;
and 4, updating and detecting the queue of the shooting area at an interval of ten seconds, and repeating the step 1 to update the queue when an unidentified face appears at an initial point or an end point by positioning according to a computer vision field target detection technology.
In a further embodiment, the queue detection unit reuses a gaussian filtering method to smooth the high-frequency information of the edge region of the input image and remove noise, and the formula is as follows:
G（x，y）=eE/2Πσ2；
Fs（x，y）=f（x，y）* G（x，y）；
wherein E = - (x)2+y2）/2σ2F (x, y) is data of an input image, and Fs (x, y) is image data after gaussian convolution.
In a further embodiment, the edge detection of the input image may cause inaccurate alignment of the queue area due to false edge, a dual threshold method is used to remove errors caused by alignment of false edges, 0.7/0.6 and 0.15 are selected as the highest threshold and the lowest threshold, respectively, and a point higher than the highest threshold is set to be 1, and a point lower than the lowest threshold is set to be 0.
In a further embodiment, as shown in fig. three, the queue-jumping behavior detection unit dynamically acquires the queuing queue frame in real time, when the queue-jumping behavior occurs, the width of the black thin-line frame may change beyond a threshold value, and is usually greater than the width of a human head frame, and the camera angle may be further adjusted or the camera pan may be further acquired by detecting the change to preliminarily lock the person trying to queue-jump.
In a further embodiment, the characteristic identification unit sends the queue-inserting person half-length frame obtained by the queue-inserting behavior detection technology to a neural network, identifies the clothes color and the gender of the queue-inserting person, converts the clothes color and the gender into text information and voice information, and broadcasts and reminds the clothes color and the gender.
The blink recognition face positioning method comprises the following contents that in order to avoid the influence of the misalignment on the queue width, face recognition is needed to remove errors, namely, faces in a queue are recognized and recorded, and when the queue width exceeds a threshold value, the error condition caused by the misalignment of a standing queue is eliminated in advance, and the method specifically comprises the following steps:
step 1, positioning face characteristic points on a picture of a detection frame by framing the picture of a shot video so as to obtain eye key points;
step 2, constructing a human eye width-height ratio model, carrying out blink detection, locking a human face when the blink frame detection probability in thirty seconds reaches 1/10-1/3, eliminating the face influence of an inactive person, and recording face data;
and 3, when the queue width exceeds the threshold value, framing the currently shot queue picture, comparing the currently shot queue picture with the result of face recognition five times before the moment after face recognition is carried out, if the number and the result of the face recognition are not changed, determining that the width is changed due to the fact that the queue is not uniform, otherwise, carrying out face hole search without recognition, and positioning the faces of the queue inserts.
In a further embodiment, the face recognition uses a 68-point feature point positioning algorithm based on a cascade regression tree, for a fixed frame picture to be detected, the algorithm generates an initial shape and initial position coordinates of 68 feature points, the sum of square errors between the initial shape and a real value is further reduced by using a gradient lifting algorithm, and the accuracy of the face recognition and the eye position positioning is ensured.
In a further embodiment, after face alignment is performed in the face recognition, different face data can be stored and can be transmitted to a neural network for training, blink judgment is performed after the eye region is further positioned, when blink judgment is unqualified, face multi-department alignment recognition is performed again, and influence of non-living person recognition is eliminated.
In a further embodiment, the face recognition record value is set in a special condition, and for a queue change caused by incomplete queuing due to direct departure of people from the queue, the face recognition record value needs to be set in a normal condition, and status exclusion is performed, specifically including the steps of:
step 1, using face recognition to identify faces in a queue, and marking faces after positioning eyes;
step 2, after face loss occurs, team face exclusion recognition is carried out, team face updating is carried out synchronously, inter-team early warning is not started, when the fact that the face loss cannot be recognized in the team is confirmed, the phenomenon that people leave uncompleted queuing tasks in the queuing queue is considered to occur, and face relocation and recognition are carried out on the queuing queue;
and 3, confirming that the queue width changes and the missing human faces disappear to be normal, and recovering normal human face recognition and blink recognition.
In summary, the present invention has the following advantages: after the initial position and the end position of a queuing queue are preliminarily confirmed by positioning a face by using a computer vision field target detection technology, reconfirming the queuing queue by using a Gaussian filtering and false edge removing method to remove the influence of redundant edges; after the queue-inserting behavior detection unit monitors the queuing queue frame in real time and the width exceeds a threshold value, positioning the queue-inserting person and identifying the face by using a blink detection method after the conditions that the queue is uneven and the queuing task is not completed and leaves halfway are eliminated; and finally, shooting and identifying specific clothing and gender information of the person inserting the team through the camera and broadcasting the information, automatically storing and processing data and transmitting the data through neural network training, thereby realizing the function of high-precision automatic detection of the non-civilized behavior of the inserting team, greatly saving the labor cost and being beneficial to promoting harmonious queuing.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition.
Claims (10)
1. A queue-insertion behavior monitoring and warning system based on human body detection is characterized by comprising a queue detection unit, a queue-insertion behavior detection unit and a feature recognition unit, wherein data of the queue detection unit, the queue-insertion behavior detection unit and the feature recognition unit are stored, processed and transmitted by utilizing neural network training;
the queue detection unit is used for carrying out basic modeling on the scenes in the set range by using Gaussian filtering, accurately positioning the queue, regulating and controlling the measurement range in real time and updating the queue to be measured along with the change of the queue;
the queue-insertion behavior detection unit is used for carrying out blink recognition on eye frames of the human faces by using frame positioning, and positioning the detected human faces; the queue-insertion behavior detection unit dynamically acquires a queue frame in real time, when queue-insertion behavior occurs, the queue width exceeds a measurement range and is usually larger than the width of a head frame of a person, the person trying to queue-insertion can be preliminarily locked by detecting the change, the angle of a camera or translation is further adjusted to acquire a shot image of the upper part of the body of the queue-insertion person, and after a non-sequential face is detected and a living body is confirmed, the non-sequential face is compared with a stored face and confirmed to be a queue-insertion face, and queue-insertion face positioning is performed;
the characteristic identification unit is used for shooting a human face by means of a camera, identifying the characteristics of the shot human face and broadcasting the characteristic to finish the warning of the queue-insertion behavior;
the queue detection unit confirms a shooting area according to the installation angle of the camera, after the shooting area is input into the system, the blue frame is used for dividing the queuing area, the computer vision field target detection technology is used for positioning a human face, the blue solid frame is used for identifying the human face, the black thin wire frame is used for confirming a detected queue, and the black thin wire frame is synchronously updated according to the change of the queuing queue range of the shooting area.
2. The human body detection-based queue behavior monitoring and warning system as claimed in claim 1, wherein the queue detection unit reuses a gaussian filtering method to smooth the high frequency information of the edge region of the input image and remove noise, and the formula is as follows:
G（x，y）=eE/2Πσ2；
Fs（x，y）=f（x，y）* G（x，y）；
wherein E = - (x)2+y2）/2σ2F (x, y) is data of an input image, and Fs (x, y) is image data after gaussian convolution.
3. The human detection-based queue activity monitoring and warning system as claimed in claim 1, wherein edge detection of the input image is due to false edge influence, a phenomenon of inaccurate alignment of queue areas occurs, a dual threshold method is used to remove errors caused by alignment of false edges, 0.7/0.6 and 0.15 are selected as a highest threshold and a lowest threshold respectively, and a point higher than the highest threshold is set to be 1, and a point lower than the lowest threshold is set to be 0.
4. The human body detection-based queue-jumping behavior monitoring and warning system as claimed in claim 1, wherein the queue-jumping behavior detection unit dynamically acquires a queuing frame in real time, when queue-jumping behavior occurs, the width of the black thin-line frame will change beyond a threshold value, usually greater than the width of a human head frame, and a shot of the upper half of the queue-jumping person can be acquired by detecting the change to preliminarily lock the person trying to queue, and further adjusting the angle of the camera or translating the camera.
5. The human body detection-based queue-jumping behavior monitoring and warning system as claimed in claim 1, wherein the feature recognition unit sends the queue-jumping person's half-body frame obtained by the queue-jumping behavior detection technology to a neural network, recognizes the clothes color and gender of the queue-jumping person, converts the clothes color and gender into text information and voice information, and broadcasts a prompt.
6. A queue-insertion behavior monitoring and warning method based on human body detection is characterized by comprising the following steps:
step 1, taking a first face identified by one side of a close-up camera head in a shooting area as a reference, taking the first face as an initial point position of a black thin line frame, and recording horizontal and vertical coordinates which are respectively 0;
step 2, taking the last face identified by one side of the telephoto image head in the shooting area as a reference, taking the last face as an end point of a black thin line frame, and recording a vertical coordinate;
step 3, performing image processing on the initial point position and the end point position by using a Gaussian filtering method, removing high-frequency information generated by a frame region due to large color change, and ensuring that all human faces in a shooting region are identified;
step 4, updating and detecting the queue of the shooting area at intervals of ten seconds, and repeating the step 1 to update the queue when an unidentified face appears at an initial point or an end point when the initial point or the end point is positioned according to a computer vision field target detection technology;
in order to avoid the influence of the queue width caused by the misalignment, the face recognition is needed to remove errors, namely, the faces in the queue are recognized and recorded, when the queue width exceeds a threshold value, the error condition caused by the misalignment of the standing queue is removed in advance, and the blink recognition face positioning method specifically comprises the following steps:
step 1, positioning face characteristic points on a picture of a detection frame by framing the picture of a shot video so as to obtain eye key points;
step 2, constructing a human eye width-height ratio model, carrying out blink detection, locking a human face when the blink frame detection probability in thirty seconds reaches 1/10-1/3, eliminating the face influence of an inactive person, and recording face data;
and 3, when the queue width exceeds the threshold value, framing the currently shot queue picture, comparing the currently shot queue picture with the result of face recognition five times before the moment after face recognition is carried out, if the number and the result of the face recognition are not changed, determining that the width is changed due to the fact that the queue is not uniform, otherwise, carrying out face hole search without recognition, and positioning the faces of the queue inserts.
7. The human body detection-based queue behavior monitoring and warning method according to claim 6, wherein the face recognition uses a cascading regression tree-based 68-point feature point positioning algorithm, for a fixed frame picture to be detected, the algorithm generates an initial shape and initial position coordinates of 68 feature points, and further uses a gradient lifting algorithm to reduce the sum of square errors between the initial shape and a real value, so that the accuracy of face recognition and eye position positioning is ensured.
8. The queue-insertion behavior monitoring and warning method based on human body detection according to claim 6, wherein the face recognition record value is set for special conditions, and for queue changes caused by incomplete queuing when people in the original queue directly leave the queue, the face recognition record value needs to be set for normal conditions, and status exclusion is performed, specifically comprising the following steps:
step 1, carrying out face recognition of a queuing queue by using face recognition, and marking faces after positioning eyes;
step 2, after face loss occurs, team face exclusion recognition is carried out, team face updating is carried out synchronously, inter-team early warning is not started, when the fact that the face loss cannot be recognized in the team is confirmed, the phenomenon that people leave uncompleted queuing tasks in the queuing queue is considered to occur, and face relocation and recognition are carried out on the queuing queue;
and 3, confirming that the queue width changes and restores to normal along with the disappearance of the missing face, and restoring normal face recognition and blink recognition.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 6 to 8 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 6 to 8.
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