Intelligent check-in system integrating face recognition and data analysis

Abstract

The present invention belongs to the field of face recognition technology, and specifically discloses an intelligent sign-in system integrating face recognition and data analysis, the sign-in system comprising a sign-in device, a face recognition terminal and a mental state evaluation terminal, the face recognition terminal is used to identify, extract and compare face information, the mental state evaluation terminal is used to collect and analyze face images, and evaluate the mental state of the sign-in personnel, the sign-in device is used to collect face information, the present invention is scientific and reasonable, safe and convenient to use, is provided with a mental state evaluation terminal, can use data analysis and machine learning to evaluate the mental state of the sign-in personnel, and can send the evaluation result to the client for display, the manager can adjust the mental state of employees with poor mental state according to the displayed mental state, reduce the probability of mistakes in work, and also let the employees feel the warmth of the enterprise, which can effectively improve the work efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of face recognition, and in particular to an intelligent check-in system integrating face recognition and data analysis. Background Art

[0002] Face recognition is a biometric technology that identifies people based on their facial features. It uses a camera or a webcam to collect images or video streams containing faces, and automatically detects and tracks faces in images, and then performs facial recognition on the detected faces.

[0003] In order to standardize the personnel management of companies, enterprises or schools, the sign-in method is often used. However, the current sign-in method has proxy signing and fake signing, which leads to confusion in personnel management. Face recognition is the safest, most convenient and standardized identification and sign-in method. The existing face recognition sign-in system has the following problems when used:

[0004] 1. The existing face recognition sign-in system can only realize the face recognition sign-in function, which is single-function and cannot understand the emotions of employees. It is impossible to understand the working status of employees, which may reduce the efficiency of employees at work and make it impossible to respond to them;

[0005] 2. Existing facial recognition devices are bulky, and the smaller facial recognition sign-in devices cannot be adjusted according to the height of the sign-in personnel;

[0006] Therefore, people urgently need an intelligent check-in system that integrates face recognition and data analysis to solve the above problems. Summary of the invention

[0007] The purpose of the present invention is to provide an intelligent sign-in system integrating face recognition and data analysis to solve the problems raised in the prior art.

[0008] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent check-in system integrating face recognition and data analysis, the check-in system comprising a check-in device, a face recognition terminal, an image processing module, a database and a mental evaluation terminal;

[0009] The face recognition terminal is used to identify, extract and compare face information, the image processing module is used to process the collected face images, the database is used to store the collected face image data and the processed face image data, the mental evaluation terminal is used to collect and analyze face images and evaluate the mental level of the sign-in personnel, and the sign-in device is used to collect face information;

[0010] The output end of the sign-in device is electrically connected to the input end of the image processing module, and the output end of the image processing module is electrically connected to the input end of the face recognition end, the database and the mental evaluation end.

[0011] As a preferred technical solution, the mental state evaluation terminal is used to evaluate the mental state of the sign-in personnel. The total score of the mental state evaluation is Z, so that the enterprise manager can choose whether to let the sign-in personnel do more important work on that day according to their mental state, which can effectively avoid mistakes in the work process due to poor mental state, causing losses to the enterprise;

[0012] The mental state evaluation end includes data analysis and machine learning. The data analysis is used to analyze the facial image data collected by the check-in device to determine the mental state of the check-in personnel. The machine learning is used to add labels to the facial graphic information entered into the database. The mental state of the check-in personnel is evaluated using two evaluation methods, making the results of the mental state evaluation more accurate and allowing the company to have a clearer understanding of the employee status.

[0013] As a preferred technical solution, the data analysis includes a model building unit, a reference plane establishing unit, a spacing measurement unit, a data comparison unit and a data calculation unit;

[0014] The model building unit is used to build a 3D face image model according to the face image data collected by the sign-in device, the reference plane establishing unit is used to build a vertical reference plane according to different positions of the established face image 3D model, the distance measuring unit is used to measure the distance between two reference planes, the data comparing unit is used to compare the distance measured by the distance measuring unit with a set threshold value, and the data calculating unit evaluates the mental state of the sign-in person according to the distance comparison;

[0015] The output end of the model building unit is electrically connected to the input end of the reference surface establishment unit, the output end of the reference surface establishment unit is electrically connected to the input end of the spacing measurement unit, the output end of the spacing measurement unit is electrically connected to the input end of the data comparison unit, the output end of the data calculation unit is electrically connected to the input end of the data comparison unit, and the data calculation unit is electrically connected to the database.

[0016] The data analysis can effectively judge the mental state of the check-in person from his facial expression. When the check-in person is in a bad mental state, his eyebrows will be frowned, which reduces the distance between his eyebrows. At the same time, when he stays up late, his eyes will be red and swollen, and he will not be able to open his eyes like normal, which makes the angle of opening of his eyes smaller.

[0017] As a preferred technical solution, the reference plane establishing unit establishes a first reference plane, a second reference plane, a third reference plane and a fourth reference plane, wherein the first reference plane and the second reference plane are vertical reference planes, the third reference plane and the fourth reference plane are horizontal reference planes, the reference point of the first reference plane is the rightmost point of the left eyebrow of the human face, the reference point of the second reference plane is the leftmost point of the right eyebrow of the human face, the reference point of the third reference plane is the lowest point of the upper eyelid, and the reference point of the fourth reference plane is the highest point of the lower eyelid;

[0018] The distance measurement unit is used to measure the distance between the first reference plane and the second reference plane and between the third reference plane and the fourth reference plane, the distance between the first reference plane and the second reference plane is X, the distance between the third reference plane and the fourth reference plane is Y, the proportion coefficient of the distance X to the total score Z of the mental evaluation is k1, the proportion coefficient of the distance Y to the total score Z of the mental evaluation is k2, and the confirmation of k1 and k2 is realized through a questionnaire survey;

[0019] The threshold of the distance X between the first reference plane and the second reference plane is m, the threshold of the distance Y between the third reference plane and the fourth reference plane is n, the change in the degree of spirituality of the unit difference between the distance X and the threshold m is a, and the change in the degree of spirituality of the unit difference between the distance Y and the threshold n is b;

[0020] According to the formula:

[0021]

[0022] Wherein, A is the spiritual degree determined by the distance X between the first reference plane and the second reference plane;

[0023] According to the formula:

[0024]

[0025] Wherein, B is the spiritual degree determined by the distance Y between the third reference plane and the fourth reference plane;

[0026] According to the formula:

[0027]

[0028] Among them, C represents the total mental value of the sign-in person;

[0029] When C ≥ c, it means that the sign-in person is in good spirits and will not affect normal work;

[0030] When C<c, it means that the sign-in person is not in a good mental state, which will affect work efficiency and even make mistakes at work. The manager can choose to adjust the staff member's time off or approve the rest.

[0031] Each set of data analyzed is compared against personal data to avoid inconsistencies in the distance between eyebrows and the angle of eye opening of each person. Only when face recognition is passed will data analysis be performed to evaluate the mental state of the sign-in personnel. For sign-in personnel with low mental state evaluation, their information and status will be sent to the client for judgment by the management personnel.

[0032] As a preferred technical solution, the machine learning includes a data retrieval unit, a data storage unit, face picture data, a label adding unit and an input unit;

[0033] The data retrieval unit is used to retrieve face image data from the database for reference by the manager. The input unit is used for the manager to input the corresponding mental state value according to the face image data of the referenced sign-in personnel. The label adding unit is used to add a label to the face image data according to the mental state value input by the input unit, indicating that the face image data is in a bad mental state. The data storage unit is used to store the face image data with the label into the database for learning by the system, and inform it that the face image data of the sign-in personnel next time is in a bad mental state.

[0034] The data retrieval unit is electrically connected to the database, and the data retrieval unit retrieves facial image data from the database. The output end of the input unit is electrically connected to the input end of the label adding unit, and the output end of the label adding unit is electrically connected to the input end of the facial image data. The output end of the facial image data is electrically connected to the input end of the data storage unit, and the output end of the data storage unit is electrically connected to the input end of the database.

[0035] By using machine learning and the manager's manual judgment, labels are added to the facial information of the sign-in personnel to reduce the frowning phenomenon during face recognition that leads to misjudgment, which can effectively improve the accuracy of mental state evaluation and provide double protection.

[0036] As a preferred technical solution, the face recognition end includes a feature extraction unit, a data conversion unit, a feature comparison unit and a result output unit;

[0037] The feature extraction unit is used to extract recognizable facial feature data from the facial graphic data collected by the sign-in device, the data conversion unit is used to convert the feature part of the facial graphic data extracted by the feature extraction unit into data that can be compared, the feature comparison unit is used to compare the data extracted by the feature extraction unit with the data stored in the data to confirm the identity of the sign-in person, and the result output unit is used to output the comparison result;

[0038] The output end of the feature extraction unit is electrically connected to the input end of the data conversion unit, the output end of the data conversion unit is electrically connected to the input end of the feature comparison unit, and the output end of the feature comparison unit is electrically connected to the input end of the result output unit.

[0039] As a preferred technical solution, the result output unit includes a display screen and a voice broadcast unit;

[0040] The display screen is used to display human faces, and the voice broadcast unit is used to voice broadcast whether the sign-in personnel have successfully signed in, and to encourage staff members with low mental state evaluation.

[0041] As a preferred technical solution, the check-in device includes a base, an L-shaped fixing plate is provided at the rear end of the base, a measuring rod is installed above the L-shaped fixing plate, an adjustment component is provided inside the base, a display panel is hingedly installed at the front end of the base, a display screen is embedded in the display panel, a camera is installed on the top of the display screen, and the camera is used to collect facial images.

[0042] As an optimal technical solution, a human infrared sensor is installed at the top of the measuring rod, and the human infrared sensor is used to detect whether the check-in person is close to the check-in device. A driving motor is installed at the bottom of the L-shaped fixed plate, and a sliding groove is opened on the upper surface of the base. One end of the driving motor is fixedly installed with a threaded rod through a key connection, and a sliding block is installed on the outer side of the threaded rod through a thread. The sliding block slides inside the sliding groove, and a support block is arranged above the sliding block. The support block is used to support the display panel. A controller is installed on the upper surface of the base, and the output end of the human infrared sensor is electrically connected to the input end of the controller, and the output end of the controller is electrically connected to the input end of the driving motor, the output end of the camera is electrically connected to the input end of the controller, and the output end of the controller is electrically connected to the input end of the display screen.

[0043] As a preferred technical solution, a rotating roller is rotatably installed on the top of the support block close to the display panel. The rotating roller is in rolling contact with the display panel to reduce the friction between the support block and the display panel, making the angle adjustment of the display panel smoother.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. A mental state evaluation terminal is set up, which can use data analysis and machine learning to evaluate the mental state of the sign-in personnel, and the evaluation results can be sent to the client for display. Managers can adjust the mental state of employees with poor mental state according to the displayed mental state, reduce the probability of errors in work, and let employees feel the warmth of the company, which can effectively improve work efficiency and reduce the problems of low work efficiency and mistakes in the work process caused by poor mental state.

[0046] 2. A sign-in device is provided. The height and angle of the camera and display screen can be effectively adjusted by means of adjustment components, so that the sign-in device can be suitable for sign-in personnel of different heights, and the facial image captured by the camera can be clearer and more comprehensive. At the same time, the friction between the sliding block and the display panel can be reduced by means of a rotating roller, so that the adjustment of the angle of the display panel by the sliding block can be smoother, and there will be no jamming during the movement. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of the module composition of an intelligent check-in system integrating face recognition and data analysis according to the present invention;

[0048] Figure 2 This is a schematic diagram of module connections of an intelligent check-in system integrating face recognition and data analysis according to the present invention;

[0049] Figure 3 This is a structural schematic diagram of a smart check-in system check-in device integrating face recognition and data analysis according to the present invention;

[0050] Figure 4 This is a structural schematic diagram of an adjustment component of an intelligent check-in system integrating face recognition and data analysis according to the present invention.

[0051] Numbers in the figure: 1. base; 2. L-shaped fixing plate; 3. driving motor; 4. sliding groove; 5. threaded rod; 6. sliding block; 7. supporting block; 8. rotating roller; 9. display panel; 10. display screen; 11. camera; 12. measuring rod; 13. human infrared sensor; 14. controller. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Embodiment 1: Figure 1-2As shown, an intelligent check-in system integrating face recognition and data analysis, the check-in system includes a check-in device, a face recognition terminal, an image processing module, a database and a mental evaluation terminal;

[0054] The face recognition terminal is used to identify, extract and compare face information, the image processing module is used to process the collected face images, the database is used to store the collected face image data and the processed face image data, the mental evaluation terminal is used to collect and analyze face images and evaluate the mental level of the sign-in personnel, and the sign-in device is used to collect face information;

[0055] The output end of the sign-in device is electrically connected to the input end of the image processing module, and the output end of the image processing module is electrically connected to the input end of the face recognition end, the database and the mental evaluation end.

[0056] The mental state evaluation terminal is used to evaluate the mental state of the sign-in personnel. The total score of the mental state evaluation is Z=100, so that the enterprise manager can choose whether to let the sign-in personnel do more important work on that day according to their mental state, which can effectively avoid mistakes in the work process due to poor mental state and cause losses to the enterprise;

[0057] The mental state evaluation terminal includes data analysis, and the data analysis is used to analyze the facial image data collected by the check-in device to determine the mental state of the check-in personnel.

[0058] The data analysis includes a model building unit, a reference plane establishing unit, a spacing measuring unit, a data comparing unit and a data calculating unit;

[0059] The model building unit is used to build a 3D face image model according to the face image data collected by the sign-in device, the reference plane establishing unit is used to build a vertical reference plane according to different positions of the established face image 3D model, the distance measuring unit is used to measure the distance between two reference planes, the data comparing unit is used to compare the distance measured by the distance measuring unit with a set threshold value, and the data calculating unit evaluates the mental state of the sign-in person according to the distance comparison;

[0060] The output end of the model building unit is electrically connected to the input end of the reference surface establishment unit, the output end of the reference surface establishment unit is electrically connected to the input end of the spacing measurement unit, the output end of the spacing measurement unit is electrically connected to the input end of the data comparison unit, the output end of the data calculation unit is electrically connected to the input end of the data comparison unit, and the data calculation unit is electrically connected to the database.

[0061] The reference plane establishing unit establishes a first reference plane, a second reference plane, a third reference plane and a fourth reference plane, wherein the first reference plane and the second reference plane are vertical reference planes, the third reference plane and the fourth reference plane are horizontal reference planes, the reference point of the first reference plane is the rightmost point of the left eyebrow of the human face, the reference point of the second reference plane is the leftmost point of the right eyebrow of the human face, the reference point of the third reference plane is the lowest point of the upper eyelid, and the reference point of the fourth reference plane is the highest point of the lower eyelid;

[0062] Based on Xiao Wang's sign-in, the distance measurement unit is used to measure the distance between the first reference plane and the second reference plane and between the third reference plane and the fourth reference plane. The distance between the first reference plane and the second reference plane is X=2.8cm, the distance between the third reference plane and the fourth reference plane is Y=0.8cm, the ratio coefficient of the distance X to the total score Z of the mental evaluation is k1=0.65, the ratio coefficient of the distance Y to the total score Z of the mental evaluation is k2=0.35, and the confirmation of k1 and k2 is achieved through questionnaire survey and comparison;

[0063] The threshold value of the distance X=2.8cm between the first reference plane and the second reference plane is m=3cm, the threshold value of the distance Y=0.8cm between the third reference plane and the fourth reference plane is n=0.9cm, the change in mental degree of 0.025cm of the unit difference between the distance X and the threshold m is a=5, and the change in mental degree of 0.05 of the unit difference between the distance Y and the threshold n is b=5;

[0064] According to the formula:

[0065]

[0066] Among them, A=55 is the spirit degree determined by the distance X=2.8 between the first reference plane and the second reference plane;

[0067] According to the formula:

[0068]

[0069] Among them, B=25 is the spirit degree determined by the distance Y between the third reference plane and the fourth reference plane;

[0070] According to the formula:

[0071]

[0072] Among them, C=80 represents the total mental level of the sign-in person;

[0073] When C≥80, it means that the sign-in person is in good spirits and will not affect normal work.

[0074] Embodiment 2: Figure 1-2As shown, an intelligent check-in system integrating face recognition and data analysis, the check-in system includes a check-in device, a face recognition terminal, an image processing module, a database and a mental evaluation terminal;

[0075] The face recognition terminal is used to identify, extract and compare face information, the image processing module is used to process the collected face images, the database is used to store the collected face image data and the processed face image data, the mental evaluation terminal is used to collect and analyze face images and evaluate the mental level of the sign-in personnel, and the sign-in device is used to collect face information;

[0076] The output end of the sign-in device is electrically connected to the input end of the image processing module, and the output end of the image processing module is electrically connected to the input end of the face recognition end, the database and the mental evaluation end.

[0077] The mental state evaluation terminal is used to evaluate the mental state of the sign-in personnel. The total score of the mental state evaluation is Z=100, so that the enterprise manager can choose whether to let the sign-in personnel do more important work on that day according to their mental state, which can effectively avoid mistakes in the work process due to poor mental state and cause losses to the enterprise;

[0078] The mental state evaluation terminal includes data analysis, and the data analysis is used to analyze the facial image data collected by the check-in device to determine the mental state of the check-in personnel.

[0079] The data analysis includes a model building unit, a reference plane establishing unit, a spacing measuring unit, a data comparing unit and a data calculating unit;

[0080] The model building unit is used to build a 3D face image model according to the face image data collected by the sign-in device, the reference plane establishing unit is used to build a vertical reference plane according to different positions of the established face image 3D model, the distance measuring unit is used to measure the distance between two reference planes, the data comparing unit is used to compare the distance measured by the distance measuring unit with a set threshold value, and the data calculating unit evaluates the mental state of the sign-in person according to the distance comparison;

[0081] The output end of the model building unit is electrically connected to the input end of the reference surface establishment unit, the output end of the reference surface establishment unit is electrically connected to the input end of the spacing measurement unit, the output end of the spacing measurement unit is electrically connected to the input end of the data comparison unit, the output end of the data calculation unit is electrically connected to the input end of the data comparison unit, and the data calculation unit is electrically connected to the database.

[0082] The reference plane establishing unit establishes a first reference plane, a second reference plane, a third reference plane and a fourth reference plane, wherein the first reference plane and the second reference plane are vertical reference planes, the third reference plane and the fourth reference plane are horizontal reference planes, the reference point of the first reference plane is the rightmost point of the left eyebrow of the human face, the reference point of the second reference plane is the leftmost point of the right eyebrow of the human face, the reference point of the third reference plane is the lowest point of the upper eyelid, and the reference point of the fourth reference plane is the highest point of the lower eyelid;

[0083] Take Xiao Li as the sign-in object, the distance measurement unit is used to measure the distance between the first reference plane and the second reference plane and the distance between the third reference plane and the fourth reference plane, the distance between the first reference plane and the second reference plane is X=2.7cm, the distance between the third reference plane and the fourth reference plane is Y=0.7cm, the proportion coefficient of the distance X to the total score Z of the mental evaluation is k1=0.55, the proportion coefficient of the distance Y to the total score Z of the mental evaluation is k2=0.45, and the confirmation of k1 and k2 is realized through a questionnaire survey;

[0084] The threshold value of the distance X=2.7 between the first reference plane and the second reference plane is m=2.65 cm, the threshold value of the distance Y=0.7 between the third reference plane and the fourth reference plane is n=0.65 cm, the change in the mental degree of the unit difference between the distance X and the threshold m is 0.05 is a=5, and the change in the mental degree of the unit difference between the distance Y and the threshold n is b=5;

[0085] According to the formula:

[0086]

[0087] Wherein, A=55 is the mental degree determined by the distance X between the first reference plane and the second reference plane;

[0088] According to the formula:

[0089]

[0090] Wherein, B=45 is the spirit degree determined by the distance Y between the third reference plane and the fourth reference plane;

[0091] According to the formula:

[0092]

[0093] Among them, C=100 represents the total mental level of the sign-in person;

[0094] When C≥80, it means that the sign-in person is in good spirits and will not affect normal work.

[0095] Embodiment 3: Figure 1-2As shown, an intelligent check-in system integrating face recognition and data analysis, the check-in system includes a check-in device, a face recognition terminal, an image processing module, a database and a mental evaluation terminal;

[0096] The face recognition terminal is used to identify, extract and compare face information, the image processing module is used to process the collected face images, the database is used to store the collected face image data and the processed face image data, the mental evaluation terminal is used to collect and analyze face images and evaluate the mental level of the sign-in personnel, and the sign-in device is used to collect face information;

[0097] The output end of the sign-in device is electrically connected to the input end of the image processing module, and the output end of the image processing module is electrically connected to the input end of the face recognition end, the database and the mental evaluation end.

[0098] The mental evaluation end includes machine learning, and the machine learning is used to add labels to the facial graphic information entered into the database.

[0099] The machine learning includes a data retrieval unit, a data storage unit, face picture data, a label adding unit and an input unit;

[0100] The data retrieval unit is used to retrieve face image data from the database for reference by the manager. The input unit is used for the manager to input the corresponding mental state value according to the face image data of the referenced sign-in personnel. The label adding unit is used to add a label to the face image data according to the mental state value input by the input unit, indicating that the face image data is in a bad mental state. The data storage unit is used to store the face image data with the label into the database for learning by the system, and inform it that the face image data of the sign-in personnel next time is in a bad mental state.

[0101] The data retrieval unit is electrically connected to the database, and the data retrieval unit retrieves facial image data from the database. The output end of the input unit is electrically connected to the input end of the label adding unit, and the output end of the label adding unit is electrically connected to the input end of the facial image data. The output end of the facial image data is electrically connected to the input end of the data storage unit, and the output end of the data storage unit is electrically connected to the input end of the database.

[0102] The face recognition end includes a feature extraction unit, a data conversion unit, a feature comparison unit and a result output unit;

[0103] The feature extraction unit is used to extract recognizable facial feature data from the facial graphic data collected by the sign-in device, the data conversion unit is used to convert the feature part of the facial graphic data extracted by the feature extraction unit into data that can be compared, the feature comparison unit is used to compare the data extracted by the feature extraction unit with the data stored in the data to confirm the identity of the sign-in person, and the result output unit is used to output the comparison result;

[0104] The output end of the feature extraction unit is electrically connected to the input end of the data conversion unit, the output end of the data conversion unit is electrically connected to the input end of the feature comparison unit, and the output end of the feature comparison unit is electrically connected to the input end of the result output unit.

[0105] The result output unit includes a display screen and a voice broadcast unit;

[0106] The display screen is used to display human faces, and the voice broadcast unit is used to voice broadcast whether the sign-in personnel have successfully signed in, and to encourage staff members with low mental state evaluation.

[0107] Embodiment 4: Figure 3-4 As shown, the check-in device includes a base 1, an L-shaped fixing plate 2 is provided at the rear end of the base 1, a measuring rod 12 is installed above the L-shaped fixing plate 2, an adjusting component is provided inside the base 1, a display panel 9 is hingedly installed at the front end of the base 1, a display screen 10 is embedded in the display panel 9, a camera 11 is installed on the top of the display screen 10, and the camera 11 is used to collect facial images.

[0108] A human infrared sensor 13 is installed at the top of the measuring rod 11, and the human infrared sensor 13 is used to detect whether the check-in person is close to the check-in device. A driving motor 3 is installed at the bottom of the L-shaped fixing plate 2, and a sliding groove 4 is opened on the upper surface of the base 1. A threaded rod 5 is fixedly installed at one end of the driving motor 3 through a key connection, and a sliding block 6 is installed on the outer side of the threaded rod 5 through a thread. The sliding block 6 slides inside the sliding groove 4, and a supporting block 7 is arranged above the sliding block 6. The supporting block 7 is used to support the display board 9. A controller 14 is installed on the upper surface of the base 1, and the output end of the human infrared sensor 13 is electrically connected to the input end of the controller 14, and the output end of the controller 14 is electrically connected to the input end of the driving motor 3, the output end of the camera 11 is electrically connected to the input end of the controller 14, and the output end of the controller 14 is electrically connected to the input end of the display screen 9.

[0109] A rotating roller 8 is rotatably mounted on the top of the support block 7 near the display panel 9. The rotating roller 8 is in rolling contact with the display panel 9 to reduce the friction between the support block 7 and the display panel 9, so that the angle adjustment of the display panel 9 is smoother.

[0110] Working principle: When using the check-in device, the human infrared sensor 13 is used to sense whether there is a check-in person approaching. When it is sensed that the check-in person is approaching, the controller 14 controls the drive motor 3 to rotate, so that the drive motor 3 drives the threaded rod 5 to rotate. The rotation of the threaded rod 5 causes the sliding block 6 to slide back and forth inside the sliding groove 4, so that the position of the support block 7 supporting the display panel 9 changes, thereby realizing the adjustment of the display angle of the display panel 9. At the same time, when the camera 11 cannot capture a complete facial image and cannot perform facial recognition, the display screen 10 transmits a signal to the controller 14, and the controller 14 controls the drive motor 3 to rotate again, and adjusts the angle of the display panel 9 again, so that the facial image captured by the camera 11 can be clearer and more complete. At the same time, the rotating roller 8 is used to reduce the friction between the support block 7 and the display panel 9, so that the adjustment of the display panel 9 is smoother.

[0111] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

Claims

1. An intelligent check-in system integrating face recognition and data analysis, characterized in that: The check-in system includes a check-in device, a face recognition terminal, an image processing module, a database and a spirit evaluation terminal; The face recognition terminal is used to identify, extract and compare face information, the image processing module is used to process the collected face images, the database is used to store the collected face image data and the processed face image data, the mental evaluation terminal is used to collect and analyze face images and evaluate the mental level of the sign-in personnel, and the sign-in device is used to collect face information; The output end of the sign-in device is electrically connected to the input end of the image processing module, and the output end of the image processing module is electrically connected to the input end of the face recognition end, the database and the mental evaluation end; The mental state evaluation terminal is used to evaluate the mental state of the sign-in personnel, and the total score of the mental state evaluation is Z; The mental state evaluation terminal includes data analysis and machine learning. The data analysis is used to analyze the facial image data collected by the check-in device to determine the mental state of the check-in personnel. The machine learning is used to add labels to the facial graphic information entered into the database. The data analysis includes a model building unit, a reference plane establishing unit, a spacing measuring unit, a data comparing unit and a data calculating unit; The model building unit is used to build a 3D face image model according to the face image data collected by the sign-in device, the reference plane establishment unit is used to build reference planes according to different positions of the established 3D face image model, the distance measurement unit is used to measure the distance between two reference planes, the data comparison unit is used to compare the distance measured by the distance measurement unit with a set threshold value, and the data calculation unit evaluates the mental state of the sign-in person according to the distance comparison; The output end of the model building unit is electrically connected to the input end of the reference plane establishing unit, the output end of the reference plane establishing unit is electrically connected to the input end of the distance measuring unit, the output end of the distance measuring unit is electrically connected to the input end of the data comparing unit, the output end of the data calculating unit is electrically connected to the input end of the data comparing unit, and the data calculating unit is electrically connected to the database; The reference plane establishing unit establishes a first reference plane, a second reference plane, a third reference plane and a fourth reference plane, wherein the first reference plane and the second reference plane are vertical reference planes, the third reference plane and the fourth reference plane are horizontal reference planes, the reference point of the first reference plane is the rightmost point of the left eyebrow of the human face, the reference point of the second reference plane is the leftmost point of the right eyebrow of the human face, the reference point of the third reference plane is the lowest point of the upper eyelid, and the reference point of the fourth reference plane is the highest point of the lower eyelid; The distance measurement unit is used to measure the distance between the first reference plane and the second reference plane and between the third reference plane and the fourth reference plane, the distance between the first reference plane and the second reference plane is X, the distance between the third reference plane and the fourth reference plane is Y, the ratio coefficient of the distance X to the total score Z of the mental evaluation is k1, the ratio coefficient of the distance Y to the total score Z of the mental evaluation is k2, and the confirmation of k1 and k2 is determined by questionnaire survey and comparison; The threshold of the distance X between the first reference plane and the second reference plane is m, the threshold of the distance Y between the third reference plane and the fourth reference plane is n, the change in the degree of spirituality of the unit difference between the distance X and the threshold m is a, and the change in the degree of spirituality of the unit difference between the distance Y and the threshold n is b; According to the formula: Wherein, A is the spiritual degree determined by the distance X between the first reference plane and the second reference plane; According to the formula: Wherein, B is the spiritual degree determined by the distance Y between the third reference plane and the fourth reference plane; According to the formula: Among them, C represents the total mental value of the sign-in person; When C ≥ c, it means that the sign-in person is in good spirits and will not affect normal work; When C < c, it means that the sign-in person is not in a good mental state, which will affect work efficiency and even make mistakes at work. The manager chooses to adjust the sign-in person to take a day off or approve a rest; The machine learning includes a data retrieval unit, a data storage unit, face picture data, a label adding unit and an input unit; The data retrieval unit is used to retrieve face image data from the database for reference by the manager. The input unit is used for the manager to input the corresponding mental state value according to the face image data of the referenced sign-in personnel. The label adding unit is used to add a label to the face image data according to the mental state value input by the input unit, indicating that the face image data is in a bad mental state. The data storage unit is used to store the face image data with the label into the database for learning by the system, and inform it that the face image data of the sign-in personnel next time is in a bad mental state. The data retrieval unit is electrically connected to the database, the data retrieval unit retrieves the face picture data from the database, the output end of the input unit is electrically connected to the input end of the label adding unit, the output end of the label adding unit is electrically connected to the input end of the face picture data, the output end of the face picture data is electrically connected to the input end of the data storage unit, and the output end of the data storage unit is electrically connected to the input end of the database; The face recognition end includes a feature extraction unit, a data conversion unit, a feature comparison unit and a result output unit; The feature extraction unit is used to extract recognizable facial feature data from the facial graphic data collected by the sign-in device, the data conversion unit is used to convert the feature part of the facial graphic data extracted by the feature extraction unit into data that can be compared, the feature comparison unit is used to compare the data extracted by the feature extraction unit with the data stored in the data to confirm the identity of the sign-in person, and the result output unit is used to output the comparison result; The output end of the feature extraction unit is electrically connected to the input end of the data conversion unit, the output end of the data conversion unit is electrically connected to the input end of the feature comparison unit, and the output end of the feature comparison unit is electrically connected to the input end of the result output unit.

2. The intelligent check-in system integrating face recognition and data analysis according to claim 1, characterized in that: The result output unit includes a display screen and a voice broadcast unit; The display screen is used to display human faces, and the voice broadcast unit is used to voice broadcast whether the sign-in personnel have successfully signed in, and to encourage staff members with low mental state evaluation.

3. The intelligent check-in system integrating face recognition and data analysis according to claim 1, characterized in that: The sign-in device comprises a base (1), an L-shaped fixing plate (2) is arranged at the rear end of the base (1), a measuring rod (12) is installed above the L-shaped fixing plate (2), an adjusting component is arranged inside the base (1), a display panel (9) is hingedly installed at the front end of the base (1), a display screen (10) is embedded and installed inside the display panel (9), and a camera (11) is installed at the top of the display screen (10).

4. The intelligent check-in system integrating face recognition and data analysis according to claim 3 is characterized in that: A human infrared sensor (13) is mounted on the top of the measuring rod (12); a driving motor (3) is mounted on the bottom of the L-shaped fixing plate (2); a sliding groove (4) is provided on the upper surface of the base (1); a threaded rod (5) is fixedly mounted on one end of the driving motor (3) via a key connection; a sliding block (6) is mounted on the outer side of the threaded rod (5) via a thread; the sliding block (6) slides inside the sliding groove (4); a supporting block (7) is arranged above the sliding block (6); a controller (14) is mounted on the upper surface of the base (1); an output end of the human infrared sensor (13) is electrically connected to an input end of the controller (14); an output end of the controller (14) is electrically connected to an input end of the driving motor (3); an output end of the camera (11) is electrically connected to an input end of the controller (14); and an output end of the controller (14) is electrically connected to an input end of a display screen (10).

5. The intelligent check-in system integrating face recognition and data analysis according to claim 4 is characterized in that: A rotating roller (8) is rotatably mounted on the top of the support block (7) on one side close to the display panel (9).

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