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Robust face recognition from single training image per person via auto-associative memory neural network

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2 Author(s)
Chuandong Wang ; College of Computer Science & Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, Jiangsu, P. R. China ; Yanying Yang

Face recognition from single training image per person is one of important challenges in appearance-based pattern recognition field. Although many existing face recognition methods have achieved success in real application, but can not be directly used to the single training image scenario. The associative memory neural networks provide a feasible strategy to address such problem. In this paper, we first briefly review the existing single training sample face recognition algorithms, and then propose a new multiple value auto-associative memory neural network by modifying evolution rule and activation function. Finally, experiments on the two publicly available face databases are provided to validate the feasibility and effectiveness of the proposed algorithm.

Published in:

Electrical and Control Engineering (ICECE), 2011 International Conference on

Date of Conference:

16-18 Sept. 2011