Neural network approach to component versus holistic recognition of facial expressions in images
Agus Rahardja, Arcot Sowmya, William H. Wilson · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
The role of features versus the whole in the learning of human facial expressions is explored. A pyramid-like modular network has been developed to learn and identify hand-drawn fKia1 expressions. Because of the nature of the network architecture, image size becomes less of an issue in network learning. The network exhibits a parallel learning capability which could be used to speed up the training process. An analysis of the hidden units of the network reveals that features are used in learning when there is commonality of facial features in the training patterns. We have also demonstrated attention focusing in the network by masking off specific areas of the face during testing. Our network model creates a "leaner" representation of the original fe object and classification is based on this representation. By including the leaner representation and separate key features in the final training set we can simulate a coarse-to-find search method, as in image processing.