Neural network recognition

EURASIP Journal on Applied Signal Processing 2003:9, 890–901 c 2003 Hindawi Publishing Corporation An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System Javad Haddadnia Engineering Department, Tarbiat Moallem University of Sabzevar, Sabzevar, Khorasan 397, Iran Email: haddadnia@sttu.ac.ir Majid Ahmadi Electrical and Computer Engineering Department, University of Windsor, Windsor, Ontario, Canada N9B 3P4 Email: ahmadi@uwindsor.

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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Comparing Robustness of Pairwise and Multiclass Neural-Network Systems for Face Recognition

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Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008, Article ID 468693, 7 pages doi:10.1155/2008/468693 Research Article Comparing Robustness of Pairwise and Multiclass Neural-Network Systems for Face Recognition J. Uglov, L. Jakaite, V. Schetinin, and C. Maple Computing and Information System Department, University of Bedfordshire, Luton LU1 3JU, UK Correspondence should be addressed to V. Schetinin, vitaly.schetinin@beds.ac.uk Received 16 June 2007; Revised 28 August 2007; Accepted 19 November 2007 Recommended by Konstantinos N.

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Artificial neural network (ANN) models have been extensively studied with the aim of achieving human-like performance, especially in the field of pattern recognition. These networks are composed of a number of nonlinear computational elements which operate in parallel and are arranged in a manner reminiscent of biological neural interconnections. ANNs are known by many names such as connectionist models, parallel distributed processing models and neuromorphic systems (Lippmann 1987).

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