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In recent years, public security agencies across the country have vigorously carried out the construction of video surveillance systems, combined with video surveillance and face recognition to achieve rapid identification and real-time control of criminal suspects, which is an important way to improve the efficiency of video surveillance. However, because the face recognition technology based on video surveillance is affected by factors such as light, angle, attitude, and occlusion, the intra-class gap of the face increases and the gap between the classes narrows. The face recognition technology based on video surveillance is applied. A huge challenge has come.
Difficulties and problems in practical applications
With the rapid development of public security video surveillance construction, the number of video surveillance has grown rapidly. There are both old analog devices and new digital high-definition devices. The face recognition technology based on video surveillance encounters many difficulties in the actual application process. Challenges include the following questions:
(1) The video image quality is relatively poor. Video images are generally acquired outdoors or indoors, and usually there is no user cooperation, so video face images often have large illumination and posture changes, and may also have occlusion.
(2) Lighting problems in face recognition. The change of illumination is the key 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 face images with non-face intrinsic attributes such as light source, occlusion and highlights, and perform targeted illumination compensation in the face image preprocessing or normalization stage to eliminate non-uniform frontal illumination. The effect of shadows, highlights, etc. on recognition performance.
(3) The problem that the face image is relatively small. Due to the poor acquisition conditions, the video face image is generally smaller than the preset size of the face recognition system based on the still image. Small-sized images not only affect the performance of the recognition algorithm, but also affect the accuracy of face detection, segmentation and key point positioning, which inevitably leads to a decline in the performance of the entire face recognition system.
(4) De-redundancy problem. It is necessary to quickly detect single and multiple face images for video capture, automatically de-redundant, subtract duplicate images, and extract corresponding face image features to achieve fast face comparison and output corresponding result information. .
(5) 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.
Typical algorithms and key technologies
The basic process of face recognition technology based on video surveillance (as shown in Figure 1) is divided into four parts: face image acquisition, face monitoring, face feature extraction and selection, and face recognition.
Fig.1 Typical algorithm of face recognition technology based on surveillance video
In Figure 1, the image source of the face image acquisition part mainly comes from the video stream in the existing video surveillance networking platform, first extracts the image frame from the video stream, and then filters the image with a more positive face from the multiple image frames. As the image to be tested. In addition, the image source may also be an image that has been manually intercepted in the monitoring platform as the image to be tested.
(1) Face detection detects whether a face exists in the image to be tested, and if it exists, marks the face. For face images with a single background, face monitoring is simpler, but it is more difficult for face monitoring in complex environments.
(2) Preprocessing of face images. Face images acquired in the natural environment are mostly affected by illumination, shooting angle, etc. Therefore, image preprocessing should be performed before image feature extraction, and reasonable image preprocessing will greatly improve the success rate of face monitoring.
(3) Feature extraction and selection is a key step in face recognition. The main basis for recognizing faces is face features. At present, there are many kinds of face features, such as: HOG method, LBP method, KL transform, and the like.
(4) Face recognition is the final recognition after the feature extraction and selection.