Facial Standardization Method in Video Face Recognition System

In order to the practical face recognition system using in an uncontrolled environment exist many outside interference factors. In this paper, it is proposed that a novel facial standardization method to extract the core region of face samples, furthermore, simultaneously adjust the facial posture and achieve face alignment. It can avoid “mis-alignment disaster” during the feature matching process. In this paper, a video face recognition system of small samples capacity has been established to test this method. The experiments show that the method can make the face samples optimal in the uncontrolled environment and enhance the recognition rate of the face recognition system. Introduction With the development of face recognition technology, some commercial face recognition systems have been put into application. Because of the limitation of face recognition method, the majority of face recognition products can only be used by users cooperate, which greatly limits the scope of face recognition application. At present, face recognition in non-cooperation and uncontrolled environment cause the most attention of researchers[1-2]. In this environment, there are many posture changes[3], illumination changes[4] and expression changes[5] for the acquired face sample images. In past time, the face recognition research works mostly focus on the effective realization of the algorithm, these algorithms are normally verify in the existing face standardization databases(such as ORL, YaleB, CMU-PIE), and have achieved very high recognition rate. However, the face samples in these databases have a fixed shooting environment, face region of uniform size and position and single hairdo. But the sample image obtained by face detection methods have different sizes, postures or positions and complex background. The face recognition algorithms under this harsh sample condition can not play maximum effectiveness, so that the recognition rate greatly reduced. For this reason, this paper proposes a novel facial standardization method, which can extract the core region of face, adjust postures, align the face features and achieve illumination and size normalization, to avoid “mis-alignment disaster” during the feature matching process. Related work About practical video face recognition algorithm, the researchers normally focus on illumination change and posture change. Compared with posture change, there are many effective solution[6] for illumination change questions, which can eliminate the effects of illumination change by illumination compensation, gradient descriptor and so on. About facial posture analysis, [7] proposes a facial reconstruction method based on deep learning and have made an excellent effect. But this method also need face alignment and large amount of calculation. [8] is based on probability and statistics thought to analyze sequential attitude changes of each person in the video. In the stage of recognition algorithm bottleneck, researches begin to pay more attention to how to exert the maximum capacity of existing algorithms in practical face recognition products. Face alignment is an important method to ensure the sample feature rectification. [9] use head pose estimation method to align facial features. [10] propose a novel cascaded regression framework for 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016) © 2016. The authors Published by Atlantis Press 654 face alignment based on a deep convolutional neural network. In addition, [11] propose a method of video-based face image quality assessment and filtration to pick out the high quality face samples for the recognition classifier in order to enhance the recognition rate. In this paper, we combine various thoughts obtained from the above methods. Face standardization process is divided into five parts: landmark localization, geometric rectification, core region extraction, size normalization and illumination normalization. The paper establishes a simple practical video face recognition system using this standardization method as its preprocessing module, the system framework shown in Figure 1. The system has three modules: face detection module, preprocessing module and face recognition module. According to the characteristic of the face sample obtained from the detection module, the paper mainly introduce the face standardization process achieved by preprocessing module, and output the face samples for the recognition module to verify. Face Detection Landmark Localization Geometric Rectification Core region Extraction