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Computational and Performance Aspects of PCA-Based Face Recognition Algorithms



H Moon, P. Jonathon Phillips


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.
NIST Interagency/Internal Report (NISTIR) - 6486
Report Number


evaluation, face recognition, PCA


Moon, H. and Phillips, P. (2001), Computational and Performance Aspects of PCA-Based Face Recognition Algorithms, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], (Accessed April 19, 2024)
Created December 31, 2000, Updated October 12, 2021