As the improvement of science and technology nowadays, face recognition, a kind of convenient, safe, and effective way of identity recognition, achieves more and more attention. Due to the unstable factors of illumination and expression in real application, there is a noticeable decline in the performance of face recognition system. In this article, we focus on the problems of the influence of illumination and expression in face recognition, and do some further research on base of Quotient Image and Local Feature Extraction. The main achievements of this article are as follows: 1. Mophological Quotient Image (MQI) based face recognition method is proposed to solve the problem of uneven illumination well. Closing operation is used to estimate the light condition. The face image is transformed to the logarithm domain, so that the key information can be promoted. In order to reduce the noise in the result, local-smoothing scheme is adopted. Furthermore, we choose the size of structure element dynamically to let it be adaptive to the local feature. Experiments on Yale B and PIE database show that such method can get a high recognition rate, and keep low computational cost as well. 2. Gradient Binary Pattern (GBP) is brought out to deal with the problem of expression variation, and we extend it to Middle & Gradient Binary Pattern (MGBP). The label of each pixel in the image is calculated. The face image is divided into several regions, and GBP histogram is extracted from each region. All the histograms are concatenated to form global feature to represent the face image, which has a lower dimension than most of current methods. Experiments on FERET database show that our method can be robust to the expression variation in face recognition, and keeps a quite stable performance when processing images with kinds of noise. In a word, our methods make a good performance to solve the problems of illumination and expression variation. Besides high face recognition rate, such methods have a low computational cost.
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