Principal component analysis (PCA) based algorithms form the basis of numerous algorithms and studies in the psychological and algorithmic face recognition literature. PCA is a statistical technique and its incorporation into a face recognition algorithm requires numerous design decisions. We explicitly state the design decisions by introducing a generic modular PCA-algorithm. This allows us to investigate these decisions including those not documented in the literature. We experiment with different implementations of each module, and evaluate the different implementations using the September 1996 FERET evaluation protocol (the de facto standard method for evaluating face recognition algorithms). We experiment with (1) changing the illumination normalization procedure; (2) studying effects on algorithm performance of compressing images using JPEG and wavelet compression algorithms; (3) varying the number of eigenvectors in the representation; and (4) changing the similarity measure in classification process. We perform two experiments. In the first experiment, we report performance results on the standard September 1996 FERET large gallery image sets. In the second experiment, we examine the variability in algorithm performance on different sets of facial images. The basis of the study is 100 randomly generated image sets (galleries) of the same size.
Citation: NIST Interagency/Internal Report (NISTIR) - 6486
NIST Pub Series: NIST Interagency/Internal Report (NISTIR)
Pub Type: NIST Pubs
evaluation, face recognition, PCA