Range Facial Recognition with the Aid of Eigenface and Morphological Neural Networks

Authors:
Chang-Wook Han
Affiliations:
Department of Electrical Engineering, Dong-Eui University, Busan, South Korea 614-714
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

Citing 5
Cited 0

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Eigenfaces for recognition

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Abstract

The depth information in the face represents personal features in detail. In particular, the surface curvatures extracted from the face contain the most important personal facial information. These surface curvature and eigenface, which reduce the data dimensions with less degradation of original information, are collaborated into the proposed 3D face recognition algorithm. The principal components represent the local facial characteristics without loss for the information. Recognition for the eigenface referred from the maximum and minimum curvatures is performed. To classify the faces, the max plus algebra based neural networks (morphological neural networks) optimized by hybrid genetic algorithm are considered. Experimental results on a 46 person data set of 3D images demonstrate the effectiveness of the proposed method.