A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Artificial Intelligence
Controlling eye movements with hidden Markov models
International Journal of Computer Vision
Real-time attention for robotic vision
Real-Time Imaging - Special issue on natural and artificial real-time imaging and vision
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Beyond Eigenfaces: Probabilistic Matching for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Journal of Cognitive Neuroscience
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Local binary patterns for multi-view facial expression recognition
Computer Vision and Image Understanding
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Data from human subjects recorded by an eyetracker while they are learning new faces shows a high degree of similarity in saccadic eye movements over a face. Such experiments suggest face recognition can be modeled as a sequential process, with each fixation providing observations using both foveal and parafoveal information. We describe a sequential model of face recognition that is incremental and scalable to large face images. Two approaches to implementing an artificial fovea are described, which transform a constant resolution image into a variable resolution image with acute resolution in the fovea, and an exponential decrease in resolution towards the periphery. For each individual in a database of faces, a hidden-Markov model (HMM) classifier is learned, where the observation sequences necessary to learn the HMMs are generated by fixating on different regions of a face. Detailed experimental results are provided which show the two foveal HMM classifiers outperform a more traditional HMM classifier built by moving a horizontal window from top to bottom on a highly subsampled face image.