Face Alignment and Recognition Under Varying Lighting and Expressions Based on Illumination Normalization
In this paper, we propose an efficient approach to perform face alignment and recognition under lighting and expression conditions based on illumination normalization. For face representation, lighting influence and variable expressions, especially the accuracy of facial localization and face recognition, are the important factors. Hence, the proposed approach aims to overcome these problems. This approach consists of two parts. One is to normalize illumination for face image. The other is to extract feature by means of principal component analysis and recognize face by means of support vector machine classifiers. Experimental results demonstrate that our approach can obtain a good facial alignment and face recognition with varying lighting, local distortion, and expressions.
KeywordsGabor-based filter Improved active shape model (IASM) Principal component analysis (PCA) Face alignment Face recognition Support vector machine (SVM)
This work was supported in part by the National Science Council of Republic of China under Grant No. NSC99-2221-E-150-064.
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