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A comparative experimental analysis of separate and combined facial features for GA-ANN based technique (2005)

Abstract

This paper investigates a feature selection and classification technique for face recognition using genetic algorithms and artificial neural networks. The experiments using separate facial features and combined facial features have been conducted on a face image dataset which is extracted from FERET benchmark database and was used in our previous study. The experiments using just combined features have also been conducted on an extended version of this dataset. The new experiments have achieved much better recognition rate than some of the existing face recognition techniques and significantly improved our previously published results. A detailed comparative analysis of experimental results is included in this paper.

Publication details

Download	http://hdl.cqu.edu.au/10018/24147
Publisher	New Jersey : IEEE,
Repository	ARROW Discovery Service (Australia)
Keywords	Information processing services. (700103), Neural Networks, Genetic Algorithms and Fuzzy Logic. (280212), TBA., Information Processing Services (incl. Data Entry and Capture) (890205), Computer Software and Services. (8902), Information and Communication Services. (89), Neural, Evolutionary and Fuzzy Computation. (080108), Artificial Intelligence and Image Processing. (0801), Information and Computing Sciences. (08), Pattern recognition systems., Algorithms., Neural networks (Computer science)
Type	conference paper
Language	en-aus
Relation	6th International Conference on Computational Intelligence and Multimedia Applications. New Jersey. : IEEE, 2005. p. 279-284 6 pages Refereed 0769523587, ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.