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Face Recognition Using 2-D, 3-D, and Infrared: Is Multimodal Better Than Multisample?

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4 Author(s)
Kevin W. Bowyer ; Dept. of Comput. Sci. & Eng., Notre Dame Univ. ; Kyong I. Chang ; Patrick J. Flynn ; Xin Chen

This work examines face recognition using normal intensity images, infrared images, three-dimensional shape, and combinations of these. We compare the performance improvement obtained by combining three-dimensional or infrared with normal intensity images (a "multimodal" approach) to the performance improvement obtained by using multiple intensity images (a "multisample" approach). Combining results from different types of imagery gives significantly higher recognition rates than are obtained by using a single intensity image. However, significantly higher recognition rates are also obtained by combining results from multiple intensity images. Overall, initial results indicate that, using an "eigen-face" recognition algorithm and weighted score fusion, multisample techniques can result in a performance increase comparable to that of multimodal techniques

Published in:

Proceedings of the IEEE  (Volume:94 ,  Issue: 11 )