Preserving Global and Local Features - a Combined Approach for Recognizing Face Images

Face recognition technologies can significantly impact authentication, monitoring and image indexing applications. Much research has been done on face recognition using global and local features over the last decade. By using global feature preservation techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), we can effectively preserve only the Euclidean structure of face space, that are devoid of the lack of local features which may play a major role in some applications. On the other hand, the local feature preservation technique namely Locality Preserving Projections (LPP) preserves local information and obtains a face subspace that best detects the essential face manifold structure; however, it also suffers loss in global features which could be important in some of the applications. In this work, a new combined approach for recognizing faces which preserve both global and local information has been introduced. The proposed technique generates Combined Global and Local Preserving Features (CGLPF) that integrates the advantages of the global feature extraction technique LDA and the local feature extraction technique LPP. He et al. in their work used PCA to extract similarity features from a given set of images in order to reduce the dimensions followed by LPP. But in our method, we use LDA (instead of PCA) to extract discriminating features to reduce the dimension that yields improved facial image recognition results. This has been verified by making a fair comparison of the above two methods by the use of ORL, UMIST and 600 images formed by combining both databases.