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Face Recognition: A Convolutional Neural Network Approach (1997)
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| Venue: | IEEE Transactions on Neural Networks |
| Citations: | 234 - 0 self |
BibTeX
@ARTICLE{Lawrence97facerecognition:,
author = {Steve Lawrence and C. Lee Giles and Ah Chung Tsoi and Andrew D. Back},
title = {Face Recognition: A Convolutional Neural Network Approach},
journal = {IEEE Transactions on Neural Networks},
year = {1997},
volume = {8},
pages = {98--113}
}
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Abstract
Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult [43]. We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sampling, a self-organizing map neural network, and a convolutional neural network. The self-organizing map provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides for partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the self-organizing map, and a multi-layer perceptron in place of the convolutional netwo...
Keyphrases
face recognition convolutional neural network approach convolutional neural network self-organizing map meaningful visual stimulus convolutional network extract topological space hybrid neural network solution karhunen-loeve transform self-organizing map neural network output space dimensionality reduction original space convolutional netwo image sample multi-layer perceptron local image sampling computational model present result hierarchical set minor change partial invariance
