Study On Accurate Face Detection And Multi-Resolution Face Recognition

The technology of face detection and recognition is one of the most widely investigated technologies in the filed of Biometric Identification, and it has been used in such areas as identity authentication, security check-up, criminal enquiry, human-computer interaction etc.In regard to face detection, proposing a detection method with high speed and accuracy remains a research hot spot. As to face recognition, due to the great variations of illumination, expression, viewpoint, age, etc. of face images, obtaining high recognition rates under these conditions still is a difficult task and research focus point. With respect to these two problems, this dissertation takes colour and gray static frontal facial images as research objects, and studies face detection and recognition methods based on combination of pattern recognition theory and image processing technology. The main content includes skin pixel extraction method on the basis of LVQ artificial neural network, face detection method using template matching technology, and a novel face recognition method employing (2D)2PCA and WPD under varying illumination, expression and pose conditions. This research targets for providing technology supports for a high-speed and accurate face recognition system. The main contributions of this research include:(1) In order to solve the problem that detection speed and accuracy of current face detection system is unbalanced, a method that extracts skin pixels using LVQ ANN and detects face based on template matching is proposed. Firstly, An LVQ ANN is used to extract skin pixels. Then, a Mosaic method is prompted to primarily locate the face region through searching within the whole image. Experiments on images from CVL indicate that the LVQ ANN gains satisfactory extraction accuracy as well as high speed, and the Mosaic method could successfully pre-locate the face region.(2) A method using template matching is adopted to detect face in the pre-located face region. First of all, a gray standard face template is gained by using only R channel of RGB images.Then, face is detected in the pre-located face region using template matching by taking relativity coefficient as the matching rule. In the end,the location and size of this face are obtained. Experimental results of three testing sets ( normal, smile and big smile sets) from CVL database show that the adopted method obtains good detection accuracy as well as speed. In the concrete, it gains 100%,100% and 93.6% correct detection rates respectively. Meanwhile, its detection speed increases from 1870.6 second/image to 208.4 second/image comparied with only adopting template matching on the original image.(3) To address the difficult problem of choosing remarkable plots from all plots gained via WPD on the original image, a method that selects"successful"plots according to the correct recognition rates (CRRs) of plots is proposed. These CRRs are obtained by combining (2D)2PCA with the nearest neighborhood classifier.(4) Aiming at efficiently fusing the feature matrixes of"successful"plots, a distance measurement between testing image and database image is presented. The L1 or L2 distances between feature matrixes of selected"successful"plots of testing image and each database image are calculated, and then taking the weighted sum of these distances as final distance. This measurement preserves both the local and global features of image, meanwhile, it also takes the CRR contribution differences of different plots into consideration. Experimental results show this measurement improves recognition performance significantly.(5) Viewing the difficulty to recognize face in images taken under different conditions, a novel recognition method employing WPD and (2D)2PCA is developed. Firstly, 20 plots are obtained via two-level WPD on the original image. Secondly, the CRRs of these plots are gained by (2D)2PCA and the nearest neighborhood classifier, and'successful'plots are selected based on these CRRs. Thirdly, the distance between testing image and each database image is calcualted using the proposed distance measurement. Finally, the nearest neighborhood classifier is adopted for recognition on the basis of this distance.(6) The proposed recognition mehod is accomplished by MATLAB 7.0 and images from CMU PIE, Yale or UMIST databases are selected to test the recognition improvement of the proposed method under different illumination, expressions and poses respectively. The performance of (2D)2PCA on the original image is defined as'standard'method. As the experimental results suggest, the proposed method obtains better performance than'standard'method under these three conditions. It performs best under different illumination whereas its performance decreases slightly under different expressions and is worst when poses change, and its highest CRRs are 98.795%, 89.796%, 36.047% respectively.(7) Observed from experimental results, the choice of distance metric has a significant effect on face recognition. In general, L1 shows higher CRRs on approximation plots, whilst L2 performs better on detailed plots. Similarly, the filters also show different performances under three different conditions. Therefore, distance metrics and filters should be selected according to these conditions. In the concrete, L1, Daubechies4, and A1, A2, H2, V2, HH2 are recommended to form the proposed method under different illumination, and L1, Haar and A2 are recommended to form the proposed method under different expressions. (8) The proposed method fails to gain satisfactory CRRs under different poses, and the highest record is 36.047%. Thus, it is necessary to seek other methods to extract facial features more efficiently.