The CSU Face Identification Evaluation System User’s Guide: Version 5.0

The CSU Face Identification Evaluation System provides standard face recognition algorithms and standard statistical methods for comparing face recognition algorithms. This document describes Version 5.0 the Colorado State University (CSU) Face Identification Evaluation System. The system includes standardized image pre-processing software, four distinct face recognition algorithms, analysis software to study algorithm performance, and Unix shell scripts to run standard experiments. All code is written in ANSII C. The preprocessing code replicates preprocessing used in the FERET evaluations. The four algorithms provided are Principle Components Analysis (PCA), a.k.a Eigenfaces, a combined Principle Components Analysis and Linear Discriminant Analysis algorithm (PCA+LDA), a Bayesian Intrapersonal/Extrapersonal Classifier (BIC), and an Elastic Bunch Graph Matching (EBGM) algorithm. The PCA+LDA, BIC, and EBGM algorithms are based upon algorithms used in the FERET study contributed by the University of Maryland, MIT, and USC respectively. Two different analysis programs are included in the evaluation system. The first takes as input a set of probe images, a set of gallery images, and similarity matrix produced by one of the four algorithms. It generates a Cumulative Match Curve that plots recognition rate as a function of recognition rank. These plots are common in evaluations such as the FERET evaluation and the Face Recognition Vendor Tests. The second analysis tool generates a sample probability distribution for recognition rate at recognition rank 1, 2, etc. It takes as input multiple images per subject, and uses Monte Carlo sampling in the space of possible probe and gallery choices. This procedure will, among other things, add standard error bars to a Cumulative Match Curve. It will also generate a sample probability distribution for the paired difference between recognition rates for two algorithms, providing an excellent basis for testing if one algorithm consistently out-performs another. The CSU Face Identification Evaluation System is available through our website and we hope it will be used by others to rigorously compare novel face identification algorithms to standard algorithms using a common implementation and known comparison techniques.