Recognizing Facial Expressions by Tracking Feature Shapes

Reliable facial expression recognition by machine is still a challenging task. We propose a framework to recognise various expressions by tracking facial features. Our method uses localized active shape models to track feature points in the subspace obtained from localized Non-negative Matrix Factorization. The tracked feature points are used to train conditional model for recognising prototypic expressions like Anger, Disgust, Fear, Joy, Surprise and Sadness. We formulate the task as a sequence labelling problem and use Conditional Random Fields(CRF) to probabilistically predict expressions. In CRF, the distribution is conditioned on the entire sequence rather than a single observation. For the joint probability defined for the entire sequence, CRF does global normalization of the exponential model, as opposed to MEMM, for which the per state exponential distribution is locally normalized. Unlike generative models(HMM), no prior dependencies between the features are assumed. We adopt...

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Atul Kanaujia, Dimitris N. Metaxas
• Keywords: Computer Vision, Facial Expressions, ICPR 2006, Prototypic Expressions, Reliable Facial Expression