Abstract
We present a novel approach to pose and illumination invariant face recognition that combines two recent advances in the computer vision field: component-based recognition and 3D morphable models. In a first step a 3D morphable model is used to generate 3D face models from only two input images from each person in the training database. By rendering the 3D models under varying pose and illumination conditions we then create a vast number of synthetic face images which are used to train a component-based face recognition system. In preliminary experiments we show the potential of our approach regarding pose and illumination invariance.
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Huang, J., Blanz, V., Heisele, B. (2002). Face Recognition Using Component-Based SVM Classification and Morphable Models. In: Lee, SW., Verri, A. (eds) Pattern Recognition with Support Vector Machines. SVM 2002. Lecture Notes in Computer Science, vol 2388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45665-1_26
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DOI: https://doi.org/10.1007/3-540-45665-1_26
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