Exemplar Hidden Markov Models for Classification of Facial Expressions in Videos
Facial expressions are dynamic events comprised of meaningful temporal segments. A common approach to facial expression recognition in video is to first convert variable-length expression sequences into a vector representation by computing summary statistics of image-level features or of spatio-temporal features. These representations are then passed to a discriminative classifier such as a support vector machines (SVM). However, these approaches don't fully exploit the temporal dynamics of...[Show more]
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