Performance analysis of Linear appearance 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 Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) for face recognition. This analysis was carried out on various current PCA, LDA and LPP 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. 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. Keywords-face recognition; Principal Component Analysis; Linear Discriminant Analysis; Locality Preserving Projections; PCA-Immune Network; Illumination Adaptive LDA; Fisher Discriminant; Subspace Discriminant LPP; Contourlet-based Locality Preserving Projection.