Improving face recognition from a single image per person via virtual images produced by imagination using neural networks - Signal and Data Processing -

Signal and Data Processing Journal A scientific journal officially licensed by the Commission for Scientific Publications of the (MSRT). Publisher: Research Ceter for Developmen of Technologies

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Volume 8, Issue 1 (9-2011) JSDP 2011, 8(1): 33-44 | Back to browse issues page

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abdolali F, dadashi N, SeyyedSalehi S. Improving face recognition from a single image per person via virtual images produced by imagination using neural networks. JSDP 2011; 8 (1) :33-44
URL: http://jsdp.rcisp.ac.ir/article-1-709-en.html

Improving face recognition from a single image per person via virtual images produced by imagination using neural networks

Fatemeh Abdolali ^*

, SeyyedAli SeyyedSalehi

Abstract: (5551 Views)

This paper deals with the problem of face recognition from a single image per person by producing virtual images using neural networks. To this aim, the person and variation information are separated and the associated manifolds are estimated using a nonlinear neural information processing model. For increasing the number of training samples in neural classifier, virtual images are produced for the neutral pose samples in a gallery dataset. By designing various structures of neural networks, the quality of virtually produced images, and consequently the recognition accuracy rate are improved. To obtain person information manifold codes giving better performance in describing the other persons and in generalizing, a learning method based on unsupervised clustering is presented. Applying this learning method and training classifier with virtual images, gives an accuracy rate of 83.63% on test dataset, which shows 12.73% improvement in comparison with training classifier using neutral pose samples.

Keywords: Face recognition, single image per person, virtual images, manifold learning, neural network, expression variant faces

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Type of Study: Research | Subject: Paper
Received: 2011/09/22 | Accepted: 2018/02/19 | Published: 2018/02/19 | ePublished: 2018/02/19

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