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Abstract: Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult (Turk and Pentland, 1991). 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... (Update)
Context of citations to this paper: More
...(Samaria Harter, 1994) contains 10 images from each of 40 individuals. The face recognition experiments presented in (Lawrence et al. 1996; Sim et al. 2000) examine accuracy by varying the size of the training set using 1, 3, or 5 images for each of the 40 classes. In...
...For instance, the ORL face dataset [8] contains 10 images from each of 40 individuals. The face recognition experiments presented in [6, 9] examine accuracy by varying the size of the training set using 1, 3, or 5 images for each of the 40 classes. In general, we define k...
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BibTeX entry: (Update)
S. Lawrence, C. L. Giles, A. C. Tsoi, A. D. Back. Face Recognition: A Hybrid Neural Network Approach, IEEE Trans. on Neural Networks, special issue on Pattern Recognition, accepted for http://citeseer.ist.psu.edu/article/lawrence96face.html More
@techreport{ lawrence96face,
author = "Steve Lawrence and C. Lee Giles and A. C. Tsoi and A. D. Back",
title = "Face Recognition: {A} Hybrid Neural Network Approach",
number = "UMIACS-TR-96-16 and CS-TR-3608",
year = "1996",
url = "citeseer.ist.psu.edu/article/lawrence96face.html" }
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