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It has been previously demonstrated that systems based on block wise local features and Gaussian mixture models (GMM) are suitable for video based talking face verification due to the best trade-off in terms of complexity, robustness and performance. In this paper, we propose two methods to enhance the robustness and performance of the GMM-ZTnorm baseline system. First, joint factor analysis is performed to compensate the session variabilities due to different recording devices, lightingdoi:10.1109/icassp.2011.5946773 dblp:conf/icassp/LiN11 fatcat:u27newmev5fj7dekcyjmai4o3q