On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Embodied conversational interface agents
Communications of the ACM
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Gesture Recognition Using Partially Observable markov Decision Processes
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Spoken dialogue management using probabilistic reasoning
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
Task oriented facial behavior recognition with selective sensing
Computer Vision and Image Understanding
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Task oriented facial behavior recognition with selective sensing
Computer Vision and Image Understanding
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This paper presents a method for learning decision theoretic models of facial expressions and gestures from video data. We consider that the meaning of a facial display or gesture to an observer is contained in its relationship to context, actions and outcomes. An agent wishing to capitalize on these relationships must distinguish facial displays and gestures according to how they help the agent to maximize utility. This paper demonstrates how an agent can learn relationships between unlabeled observations of a person's face and gestures, the context, and its own actions and utility function. The agent needs no prior knowledge about the number or the structure of the gestures and facial displays that are valuable to distinguish. The agent discovers classes of human non-verbal behaviors, as well as which are important for choosing actions that optimize over the utility of possible outcomes. This value-directed model learning allows an agent to focus resources on recognizing only those behaviors which are useful to distinguish. We show results in a simple gestural robotic control problem and in a simple card game played by two human players.