Abstract
We present a new technique for face recognition. Two distinct and mutually exclusive classes of difference between two facial images are defined: within-class differences set (differences in appearance of the same individual) and between-class differences set (differences in appearance between different individuals). Then Gaussian mixture models (GMMs) are used to estimate the eigenspace densities of the two classes. And subsequently a matching similarity measure is computed based on the maximum likelihood (ML) method. The new method achieved as much as 45% error reduction compared to the standard eigenface approach on the ORL database.
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Liao, P., Gao, W., Shen, L., Chen, X., Shan, S., Zeng, W. (2001). Classification of Facial Images Using Gaussian Mixture Models. In: Shum, HY., Liao, M., Chang, SF. (eds) Advances in Multimedia Information Processing — PCM 2001. PCM 2001. Lecture Notes in Computer Science, vol 2195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45453-5_93
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DOI: https://doi.org/10.1007/3-540-45453-5_93
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