Hidden Conditional Random Fields for Gesture Recognition

We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorporate hidden structures have proven to be advantageous for recognition tasks. Most existing approaches to gesture recognition with hidden states employ a Hidden Markov Model or suitable variant (e.g., a factored or coupled state model) to model gesture streams; a significant limitation of these models is the requirement of conditional independence of observations. In addition, hidden states in a generative model are selected to maximize the likelihood of generating all the examples of a given gesture class, which is not necessarily optimal for discriminating the gesture class against other gestures. Previous discriminative approaches to gesture sequence recognition have shown promising results, but have not incorporated hidden states nor addressed the problem of predicting the label of an entire sequence. In t...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Sy Bor Wang, Ariadna Quattoni, Louis-Philippe Morency, David Demirdjian, Trevor Darrell
• Keywords: Computer Vision, CVPR 2006, Discriminative Sequence Model, Generative Hidden State, Gesture Class, Hidden Markov Model, Hidden State Structure