Abstract
In this paper a multi-manifold approach is proposed for dealing with facial variations and limited sample availability in face recognition. Face recognition has long been an active topic in computer vision. There have been many methods proposed and most only consider frontal images with controlled lighting. However, variations in environment, pose, expression, constitute huge problems and often make practical implementation unreliable. Although these variations have been dealt with separately with varying degrees of success, a coherent approach is lacking. Here practical face recognition is regarded as a multi-view learning problem where different variations are treated as different views, which in turn are modeled by a multi-manifold. The manifold can be trained to capture and integrate various variations and hence to render a novel variation that can match the probe image. The experimental results show significant improvement over standard approach and many existing methods.
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Zaki, S.M., Yin, H. (2015). Multi-manifold Approach to Multi-view Face Recognition. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_43
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DOI: https://doi.org/10.1007/978-3-319-24834-9_43
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