With the continuous development of information technology, video information is increasingly used in various fields such as entertainment, education, security, and life. This paper introduces the research direction, application field and technical advantages of face recognition technology and makes a useful discussion on the architecture, key technologies and algorithms of face recognition technology applied in video surveillance system, especially for correcting face image technology with rotation angle. Made a more detailed statement. Finally, it is concluded that face recognition technology can be applied to the monitoring system. The intelligent video surveillance system based on face recognition technology should have a very broad application prospect.
1 Application status of video surveillance system
The development of video surveillance systems has evolved through three phases: the first-generation full-emulation system, the second-generation partially digital system, and the third-generation fully digital system (network camera and video server). The existing digital video surveillance system realizes the digitization, networking and integration of video surveillance methods, but it has one of the most important defects: the video content can only be judged by people, and at the same time, it is mostly used for "post-processing". The initiative of the video surveillance system cannot be fully utilized. Based on advanced biometrics
The emergence of face recognition intelligent video surveillance system is another sign of the development of video surveillance system. The intelligent video surveillance system can identify different objects, discover abnormalities in the surveillance picture, and can issue alarms in the fastest and best way. Provide useful information to more effectively assist security personnel in dealing with crises and minimize false positives and false negatives.
2 face recognition technology
2.1 Research and application of face recognition technology.
Face Recognition (Face RecogniTIon) is also called the basic function of human visual system and the most direct means for human recognition. Therefore, it is an important research content in biometric recognition. As an emerging biometric technology, face recognition technology is an automatic identification technology based on human facial features. Face recognition uses a variety of techniques such as digital image/video processing, pattern recognition, and computer vision. Face recognition technology has broad application prospects in the fields of public safety, human-computer interaction, etc., which has been recognized by the world. At the same time, face recognition is also a major research topic in the field of artificial intelligence, so it has attracted a large number of researchers to conduct in-depth research on this, and now has more than 30 years of research history. Since the 1990s (especially after the "911" terrorist attacks in the United States), face recognition technology has been greatly developed in research and application. The research scope of face recognition can be roughly divided into the following aspects:
(1) Face DetecTIon: detects the presence of a face from various scenes and determines its position. In most cases, because the scene is more complicated, the position of the face is not known in advance, so it is first necessary to determine whether there is a face in the scene, and if there is a face, then determine the position of the face in the image. Facial hair, cosmetics, light, noise, facial tilt and face size changes, and various occlusions can complicate face detection problems. The main purpose of face detection is to find the face area on the input whole image, and divide the image into two parts 2 face area and non-face area, which lays a foundation for subsequent processing.
(2) Face Representation (FaceRepresentaTIon): a representation is used to represent the detected face and the known face in the database. Common notations include geometric features (such as Euclidean distance, curvature, angle), algebraic features (such as matrix feature vectors), fixed feature templates, feature faces, moiré maps, and so on.
(3) FaceIdenTIfication: compares the detected face to be recognized with the known face in the database to obtain relevant information. The core of this process is to select the appropriate face representation. With the matching strategy, the structure of the system is closely related to the way the face is characterized. Usually either choose a global method or choose a feature-based approach to match. Obviously, the features selected based on the side image and the features based on the front image are quite different.
(4) Expression Analysis: The expression information (happiness, sadness, fear, surprise, etc.) of the face to be recognized is analyzed and classified.
(5) Physical Classification: It analyzes the physiological characteristics of the face to be recognized, and obtains information about race, age, gender, occupation, and so on. Obviously, doing this requires a lot of knowledge and is usually very difficult and complicated.
2.2 Advantages of face recognition technology.
Face recognition is an emerging bio-met rics technology. Compared with iris recognition, fingerprint scanning and palm-shaped scanning, face recognition technology has unique advantages in application:
(1) Easy to use, high user acceptance. (2) Intuitive. (3) The recognition accuracy is high and the speed is fast. (4) It is not easy to counterfeit. (5) Use a universal device. (6) Basic information is readily available.
3 Face Recognition Video Surveillance System Architecture
The face recognition video surveillance system has four core components: video processing/face capture workstation, face comparison workstation, blacklist database and alarm display workstation. Video processing/face capture: finding a face in a video image, evaluating image quality and submitting it to a face recognition comparison module; a face recognition comparison module: extracting a feature template from a registered photo and comparing it with a blacklist database; Blacklist photo collection: Create a template and add template data to the blacklist database; alarm display: display the alarm result according to the comparison result, or transmit the alarm information to the PDA or other portable terminal.
4 Key issues of face recognition monitoring system
(1) Illumination problems in face recognition.
The change of illumination is the most important factor affecting the performance of face recognition. The degree of resolution of this problem is related to the success or failure of the process of face recognition. It is necessary to separate the inherent face attributes from the non-face intrinsic attributes such as light source, occlusion and highlight from the face image, and perform targeted illumination compensation in the face image preprocessing or normalization stage to eliminate non-uniformity. Shadows, highlights, etc. caused by frontal illumination affect the recognition performance;
(2) Face detection and tracking problems.
Face detection is the preliminary work of face recognition, and face tracking is based on the result of face detection and positioning, and the tracking and detection of the target trajectory and contour changes in the subsequent frames of the motion sequence are continuously detected. A face detection and tracking system with multi-level structure in a complex background can adopt face detection techniques such as template matching, feature sub-face, color information, etc., so that it can detect a rotating face in a plane and can track the motion of an arbitrary posture. human face.
(3) Going to the problem of redundancy.
The face recognition monitoring system is required to quickly detect single and multiple face images for video capture, and automatically remove redundancy, subtract duplicate images, and extract corresponding face image features to achieve a faster face ratio. Right, and output the corresponding result information.
(4) Attitude problem in face recognition.
The pose problem involves facial changes caused by the rotation of the head about three axes in a three-dimensional vertical coordinate system, where depth rotation in two directions perpendicular to the image plane causes partial loss of facial information. One solution is a method based on attitude invariant features that seeks features that do not change with changes in attitude. Another solution is to use a statistical-based visual model to correct the input pose image to a frontal image so that features can be extracted and matched in a uniform pose space.
5 Conclusion
With the development of biometric technology, face recognition technology is gradually transferred from the process of theoretical exploration to the stage of practical application. Professional face recognition products have appeared at home and abroad. Face recognition technology has broad application prospects, and has typical applications in public safety, intelligent access control, intelligent video surveillance, public security control, and customs identification. The intelligent video monitoring system based on face recognition technology can effectively solve some problems existing in the current digital monitoring system, such as determining whether there is someone in the monitoring scene, difficulty in tracking the monitoring object, and determining the identity of the current monitoring object.
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