Face Recognition For Video Image Based On Single-sample Registered

Face recognition technology have improved by leaps and bounds in recent years. Now researchers focus on the study of multi-pose and multi-sample face recognition, but the method to obtain these images is very different. And the single front face image per person is easy to access. So it is very significant to study the face recognition with single training sample. This paper introduces the face recognition of video image based on single sample registered. The main works were as follows:First, it is discusses that the developing history, research situation and application fields on face image detection and recognition. This paper gives a survey of existing face recognition methods in recent years.Second, this paper introduces face detection using face classifier based on Haar features in the detecting images. When the face has been detected, using the trained binocular classifier extract the binocular region in face region. In order to more exact divide the eye areas, combine with Gabor filter and eye-template (left eye template and right eye template) matching accurately finds the eyes coordinates. According to the eyes geometric distribution in face, geometric normalize and grayscale normalize the face image to improve the face recognition rate.Last, recognize and classify the face images by single-sample registered feature extraction with wavelet transform. Using the methods of wavelet transformation and image fusion, obtain the low frequency information of registered image and deposit it to library and obtain the high frequency information of test image, then fuse the low frequency information in library and the high frequency to a fusion image. The compatibility (Euclidean distance) between fused image and test image as classification feature, classify the test image by SVM.To experiment on partial color face image in FERET and the 11 groups video image acquired by ordinary camera with the methods discussed above, achieved a good classify recognition. Extensive experiments and statistical analysis illustrates that these methods have strong robustness, high correct recognition rate and correct the rejection rate influencing by the illumination, posture, facial expression, decorations.