Biologically inspired receptive fields arc used to process input facial expressions in a modular network architecture. Local receptive fields constructed with a modified Hcbbian rule (CBA) arc used to reduce the dimensionality of input images while preserve some topological structure. In a second stage, specialized modules trained with backpropagation classify the data into the different expression categories. Thus, the neural net architecture includes 4 layers of neurons, that we train and test with images from the Yale Faces Database. A generalization rate of 82.9% on unseen faces is obtained and the results are compared to values obtained with a PCA learning rule at the initial stage.
CITATION STYLE
Jerez, J. M., Franco, L., & Molina, I. (2003). CBA generated receptive fields implemented in a facial expression recognition task. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2686, pp. 734–741). Springer Verlag. https://doi.org/10.1007/3-540-44868-3_93
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