CN112232219A - Face recognition check-in system based on LBP (local binary pattern) feature algorithm - Google Patents
Face recognition check-in system based on LBP (local binary pattern) feature algorithm Download PDFInfo
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- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
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Abstract
The invention discloses a face recognition check-in system based on an LBP (local binary pattern) characteristic algorithm, which comprises an image acquisition and processing system, a control system, a two-axis motion system and a human-computer interaction system, wherein the image acquisition and processing system is used for acquiring images; the image acquisition and processing system is used for acquiring real-time images, screening out face images in the real-time images, sequentially performing gray processing and LBP (local binary pattern) feature extraction on the face images, generating face real-time coordinates and classifying the face real-time coordinates to generate a classification result; the control system is used for receiving the real-time coordinates and the classification result of the human face, controlling the movement of the biaxial motion system according to the classification result and sending the classification result to the human-computer interaction system in real time; the two-axis motion system is used for receiving and executing a motion instruction of the control system and tracking the human face or the motion homing in real time; and the human-computer interaction system is used for displaying the classification result. The invention can intelligently search the face position, does not need a user to intentionally identify the camera, can track the unidentified face in real time, and can record the sign-in time and sign-in person information in real time.
Description
Technical Field
The invention relates to the field of image processing, in particular to a face recognition check-in system based on an LBP (local binary pattern) feature algorithm.
Background
Face recognition is a biometric technology for identity recognition based on facial feature information of a person. A series of related technologies, also commonly called face recognition and face recognition, are used to collect images or video streams containing faces by using a camera or a video camera, automatically detect and track the faces in the images, and then perform face recognition on the detected faces.
In the current life, face recognition is widely applied to identity authentication at a mobile phone end. No matter in a busy railway station, the payment is precious and quick, and the body shadow of the face recognition is everywhere. Although there are many applications of face recognition, the common face check-in system is cumbersome to use, and needs to stand at a fixed position for photographing and recognition, and many small-sized enterprises, schools, and other common companies have staff to work or students to learn, and the face check-in system is not popularized. When students sign in a university classroom, the students often need to check in by class committee in a roll-call manner, which wastes time and labor. The check-in mode in the laboratory is that the campus card is swiped to check in usually, and efficiency is not as high as that of face recognition.
Disclosure of Invention
The invention aims to provide a face recognition check-in system based on an LBP (local binary pattern) characteristic algorithm, which can intelligently search the face position, does not need a user to intentionally recognize a camera, can track the unidentified face in real time, can record the check-in time and the check-in information in real time, and is convenient for checking and counting the working time and the check-in condition of staff.
In order to solve the technical problems, the technical scheme of the invention is a face recognition check-in system based on an LBP (local binary pattern) feature algorithm, which comprises an image acquisition and processing system, a control system, a two-axis motion system and a human-computer interaction system; wherein,
the image acquisition and processing system is used for acquiring real-time images, screening face images in the real-time images by utilizing a Haar-cascade detection algorithm, generating face real-time coordinates by sequentially carrying out gray processing and LBP (local binary pattern) feature extraction on the face images, and classifying the face real-time coordinates by an SVM (support vector machine) to generate a classification result;
the control system is used for receiving the real-time coordinates and classification results of the human face, controlling the movement of the biaxial motion system according to the classification results and sending the classification results to the human-computer interaction system in real time;
the two-axis motion system is used for receiving and executing a motion command of the control system, and the motion command is specifically divided into: when the classification result shows that the undetected face exists, the control system calculates the motion trail of the biaxial motion system according to the real-time coordinates of the face by the PID algorithm, and the biaxial motion system tracks the face in real time according to the motion trail; when the classification result shows that no undetected human face exists, the motion of the two-axis motion system is returned;
and the human-computer interaction system is used for displaying the classification result.
Furthermore, the system also comprises a detection system, wherein the detection system comprises a temperature detection unit, an electric energy detection unit and a serial port connection detection unit, and is used for acquiring temperature data, system electric quantity data and serial port connection data of the control system.
Further, image acquisition and processing system includes camera module and OPENMV treater, the camera module gathers real-time image and sends to the OPENMV treater, the OPENMV treater utilizes Haar-cascade detection algorithm to select the face image in the real-time image, draws face image via grey scale processing and LBP characteristic in proper order, generates the real-time coordinate of people's face, classifies the real-time coordinate of people's face through SVM, generates classification result.
Furthermore, the human-computer interaction system is also used for displaying temperature data of the control system, electric quantity data of the system and serial port connection data.
Furthermore, the human-computer interaction system comprises an HMI serial port screen for displaying classification results, controlling system temperature data, system electric quantity data and serial port connection data.
Furthermore, the human-computer interaction system comprises a PC end, and the PC end is used for displaying the classification result and manually controlling the control system.
Furthermore, the human-computer interaction system further comprises a mobile phone end, the mobile phone end is used for displaying the classification result, and the mobile phone end is communicated with the PC end through a wireless communication module.
Furthermore, the mobile phone end and the PC end communicate through a Bluetooth module.
Still further, the camera module is an OV7725 camera module.
Furthermore, the HMI serial port screen is an OLED display.
Compared with the prior art, the invention has the beneficial effects that:
the system can intelligently find the face position without the need of recognizing the camera by the user. A set of human-computer interaction system and UI interaction interface design is constructed; the control system automatically controls the motion of the biaxial motion system, and can track unidentified human faces in real time; the system can record the attendance time in real time, the attendance information is displayed on the HMI serial port screen, and the attendance result is fed back to the mobile phone end through the wireless communication module, so that the attendance time and the attendance condition of the staff are checked and counted conveniently.
Drawings
FIG. 1 is a block diagram of the architecture of an embodiment of the present invention;
FIG. 2 is a software flow block diagram of an embodiment of the present invention;
FIG. 3 is a diagram of a check-in result page for an HMI UI interaction interface in an embodiment of the invention;
FIG. 4 is a face recognition page view of an HMI UI interaction interface in an embodiment of the invention;
FIG. 5 is a system data display page view of an HMI UI interaction interface in an embodiment of the invention.
Detailed Description
In order to facilitate the understanding and implementation of the present invention for those of ordinary skill in the art, the present invention is further described in detail with reference to the accompanying drawings and examples, it is to be understood that the embodiments described herein are merely illustrative and explanatory of the present invention and are not restrictive thereof.
Example (b):
a face recognition check-in system based on LBP characteristic algorithm is shown in figure 1 and comprises an image acquisition and processing system, a control system, a two-axis motion system, a human-computer interaction system and a detection system; wherein,
the image acquisition and processing system is used for acquiring real-time images, screening face images in the real-time images by utilizing a Haar-cascade detection algorithm, generating face real-time coordinates by sequentially carrying out gray processing and LBP (local binary pattern) feature extraction on the face images, and classifying the face real-time coordinates by an SVM (support vector machine) to generate a classification result;
the control system is used for receiving the real-time coordinates and classification results of the human face, controlling the movement of the biaxial motion system according to the classification results and sending the classification results to the human-computer interaction system in real time;
the two-axis motion system is used for receiving and executing a motion command of the control system, and the motion command is specifically divided into: when the classification result shows that the undetected face exists, the control system calculates the motion trail of the biaxial motion system according to the real-time coordinates of the face by the PID algorithm, and the biaxial motion system tracks the face in real time according to the motion trail; when the classification result shows that no undetected human face exists, the motion of the two-axis motion system is returned;
and the human-computer interaction system is used for displaying the classification result.
The detection system comprises a temperature detection unit, an electric energy detection unit and a serial port connection detection unit and is used for acquiring temperature data, system electric quantity data and serial port connection data of the control system.
Further, image acquisition and processing system includes camera module and OPENMV treater, the camera module gathers real-time image and sends to the OPENMV treater, the OPENMV treater utilizes Haar-cascade detection algorithm to select the face image in the real-time image, draws face image via grey scale processing and LBP characteristic in proper order, generates the real-time coordinate of people's face, classifies the real-time coordinate of people's face through SVM, generates classification result.
Furthermore, the human-computer interaction system is also used for displaying temperature data of the control system, electric quantity data of the system and serial port connection data.
Furthermore, the human-computer interaction system comprises an HMI serial port screen for displaying classification results, controlling system temperature data, system electric quantity data and serial port connection data.
Furthermore, the human-computer interaction system comprises a PC end, and the PC end is used for displaying the classification result and manually controlling the control system.
Furthermore, the human-computer interaction system further comprises a mobile phone end, the mobile phone end is used for displaying the classification result, and the mobile phone end is communicated with the PC end through a Bluetooth module.
Still further, the camera module is an OV7725 camera module.
Furthermore, the HMI serial port screen is an OLED display.
As shown in fig. 2, in a software work flow in a control cycle of the main control chip, data of the serial port screen is received first, so that a work page where the current system is located is determined. If the face is a face recognition page, the system enters a face recognition state, firstly, signals are sent to an OPENMV sensor, and image acquisition, face detection, LBP feature extraction and SVM classification recognition are completed through the OPENMV sensor. And finally, sending the face recognition result and the face real-time coordinate to a main control chip. If the identification result is fed back to the page, the system receives data sent by the OPENMV sensor, sends the data to the HMI serial port screen through the serial port for display, feeds back the check-in result to the serial port screen, and feeds back a specific signal to the main control chip after the serial port screen receives the check-in data. If the system data page is the system data page, the system enters a data acquisition state, the master control temperature, the system electric quantity surplus and the serial port connection state are acquired through the sensors respectively, the data are processed and then sent to the HMI serial port screen, the system data are fed back to the serial port screen, and the serial port screen receives a system data feedback specific signal and sends the system data feedback specific signal to the master control chip.
Under the condition of a face recognition page, when the main control chip receives face coordinate data of the OPENMV sensor, the real-time tracking or the homing of the two motion states is judged and executed through analyzing the data. And when the returned data processing result has no undetected human face, the main control chip controls the steering engine to move, so that the system returns to the original position and waits for the data feedback of the next period. When the returned data processing result has the face which is not detected, the PID controls the steering engine to move, so that the system is close to the face coordinate, and the face real-time tracking of the system is realized. And finally, updating OLED data display, and sending the check-in result to the mobile terminal.
The two-axis pan-tilt homing is realized by an origin coordinate reference point after system initialization, and after the system is started, the image acquisition system identifies a cross reference center placed outside the system and takes the cross reference center as the origin of coordinates. When the system executes two-axis movement homing, the main control chip controls the movement of the steering engine through a PID algorithm, and the steering engine approaches to the origin of coordinates, so that two-axis movement homing is realized. When the system executes two-axis motion to track the face, the main control chip acquires real-time coordinates of the face through the image acquisition unit, determines absolute position coordinates of the center of the face after processing data, controls the motion of the steering engine through a PID algorithm, and approaches to a target position, so that the real-time tracking of the face is realized.
The HMI (human machine interface) shown in figures 3-5 has three functions of face recognition, sign-in result display and system data display. The HMI can feed back the information and the matching number of each recognized face and the difference degree under the face recognition page, and a start stop recognition button is provided, and a user presses the button to enter a face recognition state. In the system data display, a user can check the system data and the serial port connection condition in real time. And in the check-in result page, the main control chip sends data to the HMI so as to display the result of the check-in personnel.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A face recognition check-in system based on an LBP characteristic algorithm is characterized by comprising an image acquisition and processing system, a control system, a two-axis motion system and a human-computer interaction system; wherein,
the image acquisition and processing system is used for acquiring real-time images, screening face images in the real-time images by utilizing a Haar-cascade detection algorithm, generating real-time face coordinates by sequentially carrying out gray processing and LBP (local binary pattern) feature extraction on the face images, and classifying the real-time face coordinates and the face images by an SVM (support vector machine) to generate classification results;
the control system is used for receiving the real-time coordinates and classification results of the human face, controlling the movement of the biaxial motion system according to the classification results and sending the classification results to the human-computer interaction system in real time;
the two-axis motion system is used for receiving and executing a motion command of the control system, and the motion command is specifically divided into: when the classification result shows that the undetected face exists, the control system calculates the motion trail of the biaxial motion system according to the real-time coordinates of the face by the PID algorithm, and the biaxial motion system tracks the face in real time according to the motion trail; when the classification result shows that no undetected human face exists, the motion of the two-axis motion system is returned;
and the human-computer interaction system is used for displaying the classification result.
2. The face recognition check-in system based on the LBP feature algorithm of claim 1, further comprising a detection system, wherein the detection system comprises a temperature detection unit, an electric energy detection unit and a serial connection detection unit, and is used for collecting control system temperature data, system electric quantity data and serial connection data.
3. The LBP feature algorithm based face recognition check-in system of claim 1, wherein the image acquisition and processing system comprises a camera module and an OPENMV processor, the camera module acquires a real-time image and transmits the real-time image to the OPENMV processor, the OPENMV processor screens out a face image in the real-time image by using a Haar-cascade detection algorithm, the face image is subjected to gray processing and LBP feature extraction in sequence to generate face real-time coordinates, and the face real-time coordinates are classified by an SVM to generate a classification result.
4. The LBP feature algorithm-based face recognition check-in system of claim 2, wherein the human-computer interaction system is further configured to display control system temperature data, system power data, and serial port connection data.
5. The LBP feature algorithm-based face recognition check-in system of claim 4, wherein said human-computer interaction system comprises an HMI serial port screen for displaying classification results, controlling system temperature data, system power data and serial port connection data.
6. The face recognition check-in system based on the LBP feature algorithm of claim 1, wherein the human-computer interaction system comprises a PC terminal, and the PC terminal is used for displaying the classification result and manually controlling the control system.
7. The face recognition check-in system based on LBP feature algorithm of claim 6, wherein said human-computer interaction system further comprises a mobile phone end for displaying the classification result, said mobile phone end and PC end communicating through wireless communication module.
8. The face recognition check-in system based on the LBP feature algorithm of claim 7, wherein the mobile phone terminal and the PC terminal communicate through a bluetooth module.
9. The LBP feature algorithm-based face recognition check-in system of claim 3, wherein the camera module is an OV7725 camera module.
10. The LBP feature algorithm-based face recognition check-in system of claim 5, wherein said HMI serial port screen is an OLED display.
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