Singular values (SVs) feature vectors of face image have been used for face recognition as the feature recently. Although SVs have some important properties of algebraic and geometric invariance and insensitiveness to noise, they are the representation of face image in its own eigen-space spanned by the two orthogonal matrices of singular value decomposition (SVD) and clearly contain little useful information for face recognition. This study concentrates on extracting more informational feature from a frontal and upright view image based on SVD and proposing an improving method for face recognition. After standardized by intensity normalization, all training and testing face images are projected onto a uniform eigen-space that is obtained from SVD of standard face image. To achieve more computational efficiency, the dimension of the uniform eigen-space is reduced by discarding the eigenvectors that the corresponding eigenvalue is close to zero. Euclidean distance classifier is adopted in recognition. Two standard databases from Yale University and Olivetti research laboratory are selected to evaluate the recognition accuracy of the proposed method. These databases include face images with different expressions, small occlusion, different illumination condition and different poses. Experimental results on the two face databases show the effectiveness of the method and its insensitivity to the face expression, illumination and posture.
With the development of biometrics technology, the recognition of human-face becomes the most acceptant way of identification. In the recent thirty years, face recognition technology gets more and more attentions. But unfortunately, most human-face recognition systems with a large-scale facial image database can’t be put into practice just because they have not enough recognition speed and precision. As a matter of fact, the recognition time will drastically increase as the number of human-face increases. In order to improve the recognition rates, we can firstly classify the large-scale facial image database into several comparatively small classes with specific criterion, and then begin recognition in the next step. If the classified class is still too big for recognition, another classification could be put into practice with other specific criterion until it adapts to recognition. This method is named as Multi-Layer Classification Method (MLCM) in our paper. In order to classify an unclassified face into a small class, a multiclass classifier must be set up. Because that the mahalanobis distance classifier follows the normal distribution, it is employed in our study. The results have shown that the integrative recognition rates have drastically increased for the large-scale facial image database.
In this paper, a novel method of Regional Facial Geometric Feature Recognition (RFGFR) is presented. With the development of biometrics technology, the recognition of human-face becomes the most acceptant way of identification. Based on the consideration that China is such a country with expansive regions, numerous peoples and different facial geometric structures and features, the six geographic regions based on facial geometric features have been classified according to China Administrative District. The 300 front face images of the Han nationality in the classified six regions have been sampled and registered into a face image gallery through a 3-D digital camera system. Subsequently, some geometric features (distance between two pupils, ratio of distance between two inner canthi to distance between two pupils and etc.) have been extracted and used as the facial feature recognition parameters. Furthermore, through lots of recognition experiments, we found that the Han people in different regions have different facial features to some extent. As a result, the feasibility and reliability of the RFGFR method are finally verified.