Selection and fusion of facial features for face recognition

Authors:
Xiaolong Fan;Brijesh Verma
Affiliations:
School of Computing Sciences, Faculty of Business and Informatics, Central Queensland University, Rockhampton, QLD 4701, Australia;School of Computing Sciences, Faculty of Business and Informatics, Central Queensland University, Rockhampton, QLD 4701, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This paper proposes and investigates a facial feature selection and fusion technique for improving the classification accuracy of face recognition systems. The proposed technique is novel in terms of feature selection and fusion processes. It incorporates neural networks and genetic algorithms for the selection and classification of facial features. The proposed technique is evaluated by using the separate facial region features and the combined features. The combined features outperform the separate facial region features in the experimental investigation. A comprehensive comparison with other existing face recognition techniques on FERET benchmark database is included in this paper. The proposed technique has produced 94% classification accuracy, which is a significant improvement and best classification accuracy among the published results in the literature.