Illumination-invariant Face Recognition by Kalman Filtering

We propose a novel algorithm for the identification of faces from image samples. The algorithm uses the Kalman filter to identify significant facial traits. Kalmanfaces are compact visual models that represent the invariant proportions of face classes. We employ the Kalmanfaces approach on the physics-based face database (provided by the University of Oulu), a collection of face images that were recorded under varying illumination conditions. Kalmanfaces show robustness against luminance changes and outperform the classic eigenfaces approach in terms of identification performance and algorithm speed. The paper discusses Kalmanfaces extraction, application, tunable parameters, experimental results and relatedwork on Kalman filter application in face recognition