Research Of Static Human Face Recognition

Face recognition is one of active research topics in the fields of pattern recognition and artificial intelligence. And feature extraction is the key of human face recognition, which directly influences the recognition result. This paper takes a research on the feature extraction algorithm.Computational amount is large when using the one-dimensional method of feature extraction, especially for large images. The two-dimensional method extracts feature directly and rapidly, but the features must be expressed with matrixes. Therefore the classification speed is affected by too many features. Hence, by combining their virtues, a new approach is proposed to recognize human faces based on 2DPCA (two-Dimensional Principal Components Analysis) and DLDA (Direct Linear Discriminant Analysis). First, 2DPCA is used to deal with original images, and then DLDA is used to compress the feature. Also the parameter selection of eigenvector is analyzed deeply in this paper. The results show that both recognition rate and classification speed are greatly improved.The traditional eigenfaces methods demand large face samples, and eigenfaces have to be retrained when additional face samples are added. Thus, it is not suitable for face recognition with requirements of fewer samples and high real time ability. For this problem, a LBP face recognition method based on combination features of global and local features is presented. Without re-training the eigenfaces, it directly extracts the face histogram features of global and local as feature for recognition. The experimental results indicate that the method has a higher recognition rate, better real time character and it was not sensitive to illumination condition.The single training image per person is also discussed in this paper. Both the methods of increasing training samples and using single training image for face recognition are especially researched. The feasibility of those methods is verified in ORL face database.