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.
CITATION STYLE
Minut, S., Mahadevan, S., Henderson, J. M., & Dyer, F. C. (2000). Face recognition using Foveal Vision. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1811, pp. 424–433). Springer Verlag. https://doi.org/10.1007/3-540-45482-9_43
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