ECAI 2004 Conference Paper

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Face Recognition Using Novel LDA-Based Algorithms

Guang Dai, Yuntao Qian

Facial feature extraction with enhanced discriminatory power plays an important role in face recognition (FR) applications. The linear discriminant analysis (LDA) is a powerful tool used for dimensionality reduction and feature extraction in FR tasks. However, the classification performance of traditional LDA is often degraded, due to two factors: 1) their classification accuracies suffer from the small sample size problem (SSSP), which widely exists in FR; 2) their Fisher discriminant criterions are not directly related to the classification ability. Recently, called direct fractional-step LDA (DF-LDA) algorithm has been proposed to solve this problem. In this paper, the limitations of DF-LDA are discussed and a novel DFLDA has been proposed to solve those problems. The novel DF-LDA has been tested, in terms of classification accuracy, on the ORL and the UMIST face databases. Results reveal that the proposed method outperforms the previous existing methods, including: the Eigenfaces, Fisherfaces, D-LDA, previous DF-LDA, and EFM methods.

Keywords: Keywords: Face recognition (FR), feature extraction, linear discriminant analysis (LDA), small sample size, problem (SSSP).

Citation: Guang Dai, Yuntao Qian: Face Recognition Using Novel LDA-Based Algorithms. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.455-459.

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ECAI-2004 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Universitat Politècnica de València on behalf of Asociación Española de Inteligencia Artificial (AEPIA) and Associació Catalana d'Intel-ligència Artificial (ACIA).