CONF ElShafey_BIOSIG_2012/IDIAP Face Verification using Gabor Filtering and Adapted Gaussian Mixture Models El Shafey, Laurent Wallace, Roy Marcel, Sébastien Face Recognition Gabor Gaussian Mixture Models (GMM) http://publications.idiap.ch/index.php/publications/showcite/ElShafey_Idiap-RR-37-2011 Related documents Proceedings of the 11th International Conference of the Biometrics Special Interest Group Darmstadt, Germany 2012 GI-Edition 397-408 1617-5468 978-3-88579-290-1 The search for robust features for face recognition in uncontrolled environments is an important topic of research. In particular, there is a high interest in Gabor-based features which have invariance properties to simple geometrical transformations. In this paper, we first reinterpret Gabor filtering as a frequency decomposition into bands, and analyze the influence of each band separately for face recognition. Then, a new face verification scheme is proposed, combining the strengths of Gabor filtering with Gaussian Mixture Model (GMM) modelling. Finally, this new system is evaluated on the BANCA and MOBIO databases with respect to well known face recognition algorithms. The proposed system demonstrates up to 52% relative improvement in verification error rate compared to a standard GMM approach, and outperforms the state-of-the-art Local Gabor Binary Pattern Histogram Sequence (LGBPHS) technique for several face verification protocols on two different databases. REPORT ElShafey_Idiap-RR-37-2011/IDIAP Face Verification using Gabor Filtering and Adapted Gaussian Mixture Models El Shafey, Laurent Wallace, Roy Marcel, Sébastien Face Recognition Gabor Gaussian Mixture Models (GMM) Idiap-RR-37-2011 2011 Idiap December 2011 The search for robust features for face recognition in uncontrolled environments is an important topic of research. In particular, there is a high interest in Gabor-based features which have invariance properties to simple geometrical transformations. In this paper, we first reinterpret Gabor filtering as a frequency decomposition into bands, and analyze the influence of each band separately for face recognition. Then, a new face verification scheme is proposed, combining the strengths of Gabor filtering with Gaussian Mixture Model (GMM) modelling. Finally, this new system is evaluated on the BANCA database with respect to well known face recognition algorithms and using both manual and automatic face localization. The proposed system demonstrates up to 47% relative improvement in verification error rate compared to a standard GMM approach, and comparable results with the state-of-the-art Local Gabor Binary Pattern Histogram Sequence (LGBPHS) technique for four of the seven BANCA face verification protocols.