Abstract
We present an appearance-based method for face recognition and evaluate its robustness against illumination changes. Self-organizing map (SOM) is utilized 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 conducting some experiments on Yale B and CMU-PIE face databases.
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This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008
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Aly, S., Tsuruta, N. & Taniguchi, RI. Face recognition under varying illumination using Mahalanobis self-organizing map. Artif Life Robotics 13, 298–301 (2008). https://doi.org/10.1007/s10015-008-0555-z
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DOI: https://doi.org/10.1007/s10015-008-0555-z