CATEGORIZATION AND IDENTIFICATION OF HUMAN FACE IMAGES BY NEURAL NETWORKS: A REVIEW OF THE LINEAR AUTOASSOCIATIVE AND PRINCIPAL COMPONENT APPROACHES
1994 ◽
Vol 02
(03)
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pp. 413-429
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Keyword(s):
Recent statistical/neural network models of face processing suggest that faces can be efficiently represented in terms of the eigendecomposition of a matrix storing pixel-based descriptions of a set of face images. The studies presented here support the idea that the information useful for solving seemingly complex tasks such as face categorization or identification can be described using simple linear models (linear autoassociator or principal component analysis) in conjunction with a pixel-based coding of the faces.
2010 ◽
pp. 138-156
2015 ◽
Vol 438
◽
pp. 178-187
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2020 ◽
Vol 161
◽
pp. 26-37
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2017 ◽
Vol 31
(4)
◽
pp. 1127-1142
◽
2000 ◽
Vol 14
(3)
◽
pp. 471-494
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Keyword(s):
2011 ◽
Vol 82
(1)
◽
pp. 25-30
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