Face image analysis and its applications

The aim of this research is to develop efficient algorithms for facial image analysis. Our research focuses on three areas: face recognition, illumination models and compensation, and facial expression recognition.; We have proposed two methods for face recognition under various conditions: Elastic Shape-Texture Matching (ESTM) and Doubly nonlinear mapping kernel Principal Component Analysis (DKPCA). ESTM uses not only the shape information but also the texture information in comparison of two faces without establishing any precise pixel-wise correspondence. DKPCA proposes a doubly nonlinear mapping kernel PCA to perform feature transformation and face recognition, which not only considers the statistical property of the input features, but also emphasizes those important facial feature points.; We also investigate and propose two model-based methods for modeling illumination on the human face. The first method can compensate for the uneven illuminations on human faces and reconstruct face images in normal lighting conditions, where a 2D face shape model is used to obtain a shape-free texture image. The second illumination compensation method aims to reduce or even remove the effect of these factors. In this method, a local normalization technique is applied to an image, which can effectively and efficiently eliminate the effect of uneven illuminations.; We have also presented an efficient method for facial expression recognition. We first propose a representation model for facial expressions, namely spatially maximum occurrence model (SMOM). The ESTM algorithm is then used to measure the similarity between images for facial expression recognition. By combining SMOM and ESTM, the algorithm is called SMOM-ESTM and can achieve a higher recognition performance level.; To reduce the computational complexity when face recognition is applied to a large-scale database, it is necessary to filter the large database to form a smaller one that contains face images similar to the query input. Therefore, we propose an efficient indexing structure for searching a human face in a large database, which can produce a condensed database including the target image and therefore reduce the search time.