Exemplar Hidden Markov Models for Classification of Facial Expressions in Videos
Project Synopsis: 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 (max/mean pooling) 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 donít fully exploit the temporal dynamics of facial expressions. Hidden Markov Models (HMMs), provide a method for modeling variable-length expression timeseries. Although HMMs have been explored in the past for expression classification, they are rarely used since classification performance is often lower than discriminative approaches, which may be attributed to the challenges of estimating generative models.
Related publication: Sikka K., Dhall A., Bartlett, M. (2015). Exemplar Hidden Markov Models for Classification of Facial Expressions in Videos. IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Analysis and Modeling Faces and Gestures. [PDF]
Results: Shown results on two datasets. Pl. refer to publication for more details.