Abstract
Identifiying human faces with only a single face model per person is a difficult task, because the input face image may vary with position, expression and pose. This paper describes a flexible face identification system based on Gabor wavelet representation and flexible neural network matching. The face is represented by a hexagonal graph of neurons where each node contains local feature information extracted by Gabor wavelet transform at the corresponding position. An innovative flexible neural network matching is employed to finding out the exact correspondence of local features between the model and the input image based on the local feature similarity and neighborhood grou** neurons. The matching process is evaluated by competition rule based on the correlation of neuron activation in the model layers and input layer. Experiments with face images that include the variations of rotation-in-plane, rotation-in-depth, and the change of facial expression are also presented.
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© 1997 Springer-Verlag Berlin Heidelberg
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Pramadihanto, D., Iwai, Y., Yachida, M., Wu, H. (1997). Identifying faces under varying pose using a single example view. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63931-4_273
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DOI: https://doi.org/10.1007/3-540-63931-4_273
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