Multi-camera systems and GPU-based stereo-matching methods allow for a real-time 3d reconstruction of faces. We use the data generated by such a 3d reconstruction for a hybrid face recognition system based on color, accuracy, and depth information. This system is structured in two subsequent phases: geometry-based data preparation and face recognition using wavelets and the AdaBoost algorithm. It requires only one reference image per person. On a data base of 500 recordings, our system achieved detection rates ranging from 95% to 97% with a false detection rate of 2% to 3%. The computation of the whole process takes around 1.1 seconds. © 2011 Springer-Verlag.
CITATION STYLE
Hensler, J., Denker, K., Franz, M., & Umlauf, G. (2011). Hybrid face recognition based on real-time multi-camera stereo-matching. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6939 LNCS, pp. 158–167). https://doi.org/10.1007/978-3-642-24031-7_16
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