A comprehensive comparative performance analysis of Laplacianfaces and Eigenfaces for face recognition
Usama Ijaz Bajwa, Imtiaz Ahmad Taj, Zeeshan Bhatti · The Imaging Science Journal · 2011
This paper provides a comprehensive comparative analysis of the performance of locality preserving projections (LPPs)‐based Laplacianfaces, which is a recently introduced algorithm with the more traditional, principal component analysis (PCA)‐based Eigenfaces. All possible combinations of neighbourhood defining distance metrics, classifier distance metrics and number of retained eigenvectors have been tried on different imaging environments. The FERET facial database was chosen which provides enough diversity in illumination, facial expressions and aging. CsuFaceIdEval, an open source platform, is used for this comparison and recognition rates are studied in detail. As a result of our detailed analysis, we provide best combination of selected parameters to extract the best results from these two algorithms.