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Abstract:
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Conditional Random Fields (CRFs ) is a discriminative and supervised approach for simultaneous sequence segmentation and frame labeling . Latent -Dynamic Conditional Random Fields (LDCRFs ) incorporates hidden state variables within CRFs which model sub -structure motion patterns and dynamics between labels . Motivated by the success of LDCRFs in gesture recognition , we propose a framework for automatic facial expression recognition from continuous video sequence by modeling temporal variations within shapes using LDCRFs . We show that the proposed approach outperforms CRFs for recognizing facial expressions . Using Principal Component Analysis (PCA ) we study the separability of various expression classes in lower dimension projected spaces . By comparing the performance of CRFs and LDCRFs against that of Support Vector Machines (SVMs ) and a template based approach , we demonstrate that temporal variations within shapes are crucial in classifying expressions especially for those with small facial motion like anger and sadness . We also show empirically that only using changes in facial appearance over time without using the shape variations fails to obtain high performance for facial expression recognition . This reflects the importance of geometric deformations on face for recognizing expressions . |