Under Variable Lighting And Occlusion Conditions, Face Recognition Technology And Its Applications

As one of the most heated research spot in the field of pattern recognition, the human face recognition applies to tremendous aspects and has a promising future. This dissertation focuses on face recognition algorithms based on statci image under variation of illumination and occlusion, special attention has been paid to the subspace, LBP, as well as spare representation.The main research results are as following:(1) Considering that, by casting image from data space to feature space, subplace based face recognition methods can absorb information that is useful for recognition from reducted date dimension, this dissertation studies its application in face recognition. The merits and demerits of seven subspace based methods are presented and the relationship between the seven methods are disclosed.(2) To alleviate the impact of variation of illumination, we bring about the method based on LBP texture feature of sub windows. Firstly we extract the LBP texture feature histogram of every window, and then we pile up every histogram, at last we acquire the characters of human face. The result confirms that this method has a steady effectiveness to the variation of illumination and requires low computational complexity.(3) This dissertation finds the method of automatically recognizing human faces from frontal views with disguise or occlusion, this is face recognition based on spare representation. The method casts the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by L1-minimization, we propose a general classification algorithm for (image-based) object recognition. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm.At the same time, under natural environment, we build a real-time face recognition system using LBP texture feature which achieves good results in the laboratory.