Performance Analysis of Linear Appearance Based Algorithms for Face Recognition
Analyzing the face recognition rate of various current face recognition algorithms is absolutely critical in developing new robust algorithms. In this paper, the authors propose performance analysis of Principal Component Analysis (PCA), Linear Discriminate 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.