ANALYSING RECOGNITION RATE OF LDA AND LPP BASED ALGORITHMS FOR FACE RECOGNITION

Analysing the face recognition rate of various current face recognition algorithms is absolutely critical in developing new robust algorithms. In his paper we propose performance analysis of Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) for face recognition. This analysis was carried out on various current LDA and LPP based face recognition algorithms using standard public databases. 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. Among various LPP algorithms analyzed, Subspace Discriminant LPP (SDLLP) provides the best face recognition rate of 98.38% on ORL database, the next best was 97.5% using Contourlet-based Locality Preserving Projection (CLPP) on ORL database.

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