Using Feature Combination and Statistical Resampling for Accurate Face Recognition Based on Frequency Domain Representation of Facial Asymmetry
7th International Conference on Automatic Face and Gesture Recognition (FGR06) (2006)
University of Southampton,UK
Apr. 10, 2006 to Apr. 12, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FGR.2006.109
Sinjini Mitra , Carnegie Mellon University
Marios Savvides , Carnegie Mellon University
This paper explores the efficiency of facial asymmetry in face 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 PCAtype classifier on a database with 55 individuals, which establishes the robustness of these measures to intra-personal distortions. Furthermore, we demonstrate that it is possible to improve upon these results significantly by simple means such as feature set combination and statistical resampling methods like bagging and Random Subspace Method (RSM) using the same PCA-type base classifier. This even succeeds in attaining perfect classification results with 100% accuracy in some cases. Moreover, both these methods require few additional resources (computing time and power), hence they are useful for practical applications as well and help establish the effectiveness of frequency domain representation of facial asymmetry in automatic identification tasks.
S. Mitra and M. Savvides, "Using Feature Combination and Statistical Resampling for Accurate Face Recognition Based on Frequency Domain Representation of Facial Asymmetry," 7th International Conference on Automatic Face and Gesture Recognition (FGR06)(FG), University of Southampton,UK, 2006, pp. 31-36.