We propose a novel algorithm for the identification of faces from image samples. The algorithm uses the Kalman filter to identify significant face features. We employ the Kalmanfaces approach on a database of face images that show a variety of different expressions and were recorded under varying lighting conditions. Kalmanfaces show robustness against distortion and outperform the classic Eigenfaces approach in terms of identification performance and algorithm speed.
H. Eidenberger: "Kalman Filtering for Robust Identification of Face Images with Varying Expressions and Lighting Conditions"; Talk: IAPR ICPR 2006, Hongkong, China; 08-13-2006 - 08-15-2006; in: "IAPR ICPR 2006 Proceedings", (2006).
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