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Lip Segmentation Using Adaptive Color Space Training
Authors: Erol Ozgur, Berkay Yilmaz, Harun Karabalkan, Hakan Erdogan, Mustafa Unel
Published in: International Conference on Auditory-Visual Speech Processing 2008
Publication year: 2008
Abstract: In audio-visual speech recognition (AVSR), it is beneficial to use lip boundary information in addition to texture-dependent features. In this paper, we propose an automatic lip segmentation method that can be used in AVSR systems. The algorithm consists of the following steps: face detection, lip corners extraction, adaptive color space training for lip and non-lip regions using Gaussian mixture models (GMMs), and curve evolution using level-set formulation based on region and image gradients fields. Region-based fields are obtained using adapted GMM likelihoods. We have tested the proposed algorithm on a database (SU-TAV) of 100 facial images and obtained objective performance results by comparing automatic lip segmentations with hand-marked ground truth segmentations. Experimental results are promising and much work has to be done to improve the robustness of the proposed method.
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