Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
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In this paper we propose a novel method for performing pose-tolerant face recognition. We propose to use Fourier Magnitude Spectra of face images as signatures and then perform principal component analysis (PCA) and Fisher-faces (LDA) leading to new representations that we call Eigen and Fisher-Fourier Magnitudes. We show that performing PCA and Fisherfaces on the Fourier magnitude spectra provides significant improvement over traditional PCA and Fisherfaces on original spatial-domain image data. Furthermore, we show analytically and experimentally that our proposed approach is shift-invariant, i.e., we obtain the same Fourier-Magnitude Spectra regardless of the shift of the input image. We report recognition results on the ORL face database showing the significant improvement of our method under many different experimental configurations including the presence of noise.