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Performance Analysis of PCA-based and LDA-based Algorithms for Face Recognition

Steven Fernandes and Josemin Bala
Department of Electronics and Communication Engineering, Karunya University, Coimbatore, India
Abstract—Analysing the face recognition rate of various current face recognition algorithms is absolutely critical in developing new robust algorithms. In his paper we report performance analysis of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for face recognition. This analysis was carried out on various current PCA and LDA based face recognition algorithms using standard public databases. Among various PCA algorithms analyzed, Manual face localization used on ORL and SHEFFIELD database consisting of 100 components gives the best face recognition rate of 100%, the next best was 99.70% face recognition rate using PCA based Immune Networks (PCA-IN) on ORL database. Among various LDA algorithms analyzed, Illumination Adaptive Linear Discriminant Analysis (IALDA) gives the best face recognition rate of 98.9% on CMU PIE database, the next best was 98.125% using Fuzzy Fisherface through genetic algorithm on ORL database.

Index Terms—face recognition; Principal Component Analysis; Linear Discriminant Analysis; PCA-IN; Illumination Adaptive LDA; Fisher Discriminant.

Cite: Steven Fernandes and Josemin Bala , "Performance Analysis of PCA-based and LDA-based Algorithms for Face Recognition," International Journal of Signal Processing Systems,  Vol. 1, No. 1, pp. 1-6,  June 2013.doi: 10.12720/ijsps.1.1.1-6
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