Learning embedded lines of attraction by self organization for pose and expression invariant face recognitionn1

Ming-Jung Seow; Ann Theja Alex; Vijayan Asari

doi:10.1117/1.OE.51.10.107201

1 October 2012 Learning embedded lines of attraction by self organization for pose and expression invariant face recognitionn1

Ming-Jung Seow, Ann Theja Alex, Vijayan Asari

Author Affiliations +

Optical Engineering, Vol. 51, Issue 10, 107201 (October 2012). https://doi.org/10.1117/1.OE.51.10.107201

Abstract

Algorithms that mimic the computation and learning capabilities of the human brain are feasible solutions to many information-processing problems. We present a theoretical model based on the observation that images of similar visual perceptions reside in a complex manifold in an image space. To model the pattern manifold, we present a novel learning algorithm using a recurrent neural network based on the behavior of the brain. In designing a recurrent neural network, convergence dynamics of the network needs special consideration. We propose to modify this picture: If the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented with an unknown encoded representation of a visual image belonging to a different category. Based on this, we have developed a self-organizing line attractor to learn new patterns. A nonlinear dimensionality reduction technique is used to embed the points to a lower dimensional space that preserves the intrinsic dimensionality and metric structure of the data to enable fast and accurate recognition. Experiments performed on UMIST, CMU AMP, FRGC version-2, Japanese female face expression, and Essex Grimace databases show the effectiveness of the proposed approach in accurate recognition of complex patterns.

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Ming-Jung Seow, Ann Theja Alex, and Vijayan Asari "Learning embedded lines of attraction by self organization for pose and expression invariant face recognitionn1," Optical Engineering 51(10), 107201 (1 October 2012). https://doi.org/10.1117/1.OE.51.10.107201

Published: 1 October 2012

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