Face recognition under varying illumination using Mahalanobis self-organizing map

We present an appearance-based method for face recognition and evaluate it with respect to robustness against illumination changes. Self-organizing map (SOM) used to transform the high dimensional face image into low dimensional topological space. However, the original learning algorithm of SOM uses Euclidean distance to measure similarity between input and codebook images, which is very sensitive to illumination changes. In this paper, we present Mahalanobis SOM, which uses Mahalanobis distance instead of the original Euclidean distance. The effectiveness of the proposed method is demonstrated by the experiments on Yale B and CMU-PIE face databases.