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Improved Human Face Identification Using Frequency Domain Representation of Facial Asymmetry

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2 Author(s)
Mitra, Sinjini ; Dept. of Stat., Carnegie Mellon Univ. ; Savvides, M.

This paper explores the role of facial asymmetry in identification tasks using a frequency domain representation. Satisfactory results are obtained for two different tasks, namely, human identification under extreme expression variations and expression classification, using a PCA-type classifier which establishes the robustness of these measures to intra-personal distortions. We next demonstrate that it is possible to even improve upon these results by simple means. In particular, we use two methods, namely, feature set combination and statistical resampling methods like bagging, which attains perfect classification results (0% error rate) in some cases. Both these methods require very few additional resources in terms of computing power, hence they are useful for practical applications as well

Published in:

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on  (Volume:2 )

Date of Conference:

14-19 May 2006