The CSU Face Identification Evaluation System: Its Purpose, Features and Structure

Transcription

1 The CSU Face Identification Evaluation System: Its Purpose, Features and Structure David S. Bolme, J. Ross Beveridge, Marcio Teixeira and Bruce A. Draper Computer Science, Colorado State University 3rd International Conference on Computer Vision Systems - ICVS 2003 The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 1

2 Goals of the CSU Face Recognition Evaluation Work Baseline/control Face Recognition algorithms. Four algorithms selected from FERET 96/97 study. PCA, Eigenfaces (Turk and Pentland, MIT) PCA+LDA, (Zhao et. al., Maryland) Bayesian Image diff. Classifier, (Moghaddam et. al., MIT) Elastic Bunch Graph (Okada, et. al., USC) Reference implementations in ANSI C. CSU Face Identification Evaluation System Statistical methodology for studying algorithms. Parametric and Nonparametric methods Standardized protocols and associated scripts. Determine critical factors that influence performance. The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 2

3 Obtaining the CSU Face Identification Evaluation System The Evaluation of Face Recognition Algorithms Website. First release of code on March 1, 2001 Current code release, Version 4.0, October 31, 2002 Over 1,500 downloads of Version 4.0 through March 2003 Users Guide is included and also available separately. The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 3

4 CSU Face Identification Evaluation System: Users Guide Installation Testing the system Scripts,scrapshots System Overview Image Formats Distance Files Image Preprocessing Algorithms PCA, PCA+LDA, BIC Analysis Cumulative Match Curves Error bars & distributions This ICVS 2003 Paper overlaps parts of the User s Guide The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 4

5 System Overview Preprocessing Normalization Training Subspace Training Bayesian Training Testing Subspace Project Bayesian Project Analysis Rank Curve Testing Permutation Testing Standard Cumulative Match Curves Probability Distribution for Recognition Rate The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 5

6 Image Preprocessing Integer to float conversion Converts 256 gray levels to single-floats Geometric Normalization Aligns human chosen eye coordinates Masking Crop with elliptical mask leaving only face visible. Histogram Equalization Histogram equalizes unmasked pixels: 256 levels. Pixel normalization Shift and scale pixel values so mean pixel value is zero and standard deviation over all pixels is one. Refinement of NIST preprocessing used in FERET. The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 6

7 The csusubspace module: PCA and PCA+LDA Training Training images Eigenspace Combined space (PCA+LDA) Testing PCA+LDA space projection Distance Matrix The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 7

8 Bayesian Image difference Classifier: Take Difference of Images Classify difference image as either: Intrapersonal from same subject Extrapersonal from different subjects - = Intrapersonal Example - = Extrapersonal Example The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 8

9 Bayesian Image difference Classifier: Training Uses csusubspace Module All Training Images csumakediffs Extrapersonal... csusubspacetrain Extrapersonal PCA Subspace... Intrapersonal csusubspacetrain Intrapersonal PCA Subspace The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 9

10 Bayesian Image difference Classifier: Testing uses csubayesianproject Extrapersonal PCA Subspace Probe & Gallery Images CsuBayesianProject Distance Matrix Intrapersonal PCA Subspace The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 10

11 Two Distinct Questions 1. Is an observed difference in performance significant? McNemar s Test. Monte Carlo Inference. 2. What covariates, and combinations of covariates, most influence performance? And how much? Evaluation Methodology and Tools Monte Carlo Inference. Generalized Linear Models. Covariates covers both features of algorithms and of people Complexity Simple Involved Weak Generalized Linear Model Example: Mixed Effects Logistic Regression with Repeated Measures on People. Version 4.0 Monte Carlo Inference Example: Sample Recognition Rate Probability Distribution created by perturbing probe gallery choice. McNemar stest Tally when one algorithm succeeds and the other fails. Power Strong The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 11

12 Training, Probes, Galleries, What Varies? Training Gallery F F F F F V F V F F V V V F F V F V V V F V V V Probes F Fixed Throughout Study V Varied, i.e. randomly sampled Essentially FERET 1996/97 Micheals & Boult CVPR 2001 CSU PCA vs. PCA+LDA Analysis CSU PCA+LDA Configuration Analysis The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 12

13 Producing Cumulative Match Curves The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 13

14 Producing Sample Distributions Compare PCA and PCA+LDA. Distance Measures: L1, L2, Mah. Angle (PCA), Soft L2 (PCA+LDA). Methodology: Monte Carlo Sampling of Probe/Gallery. Training Testing - Galleries and Probes Balanced Sampling Subject Subject CVPR 2001 citation. Day 1 Day 2 Id P P G G 2 P P G G G 3 G G P P 4 G G P P P The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 14

15 PCA vs. PCA+LDA Confidence Intervals Sample Probability Distribution for PCA at rank 1 using Mahalanobis Distance Probability The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 15

16 Tabular Output from csupermute The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 16

17 PCA vs. PCA+LDA Comparing Distance Measures Distance Measure Matters PCA favors Mahalanobis Angle PCA+LDA, Soft and Angle Similar Cumulative Match with Error Bars Distance choice more important than subspace. The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 17

18 Current Research FERET Subject Covariates Covariates for 2,974 Images, 1,209 Subjects FERET Subject/Image Covariates Fixed Per Subject Age Young Old Gender Male Female Race White Black Asian Other Skin Clear Other Fixed Per Image Bangs No Yes Expression Neutral Other Eyes Open Other Facial Hair No Yes Makeup No Yes Mouth Closed Other The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 18

19 FERET Covariates Results (Preliminary!) Glasses Off/On Glasses Off Glasses Always On Age Young Age Old Harder to Recognize Eyes Open/Closed Expression Changes Facial Hair Changes Always Makeup Makeup Changes Mouth Always Open Mouth Changes Eyes Open Expression Neutral Race White No Facial Hair No Makeup Mouth Closed Eyes Always Closed Always Non-neutral Race Asian Race African-Amer. Race Other Always Facial Hair Easier to Recognize Bangs Change No Bangs Always Bangs Skin Not Clear Skin Clear Male Female -50% -40%-30%-20% -10% 0% 0% 10% 20% 30% 40% 50% Change in Similarity Measure The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 19

20 Conclusion Release 4.0 Contains Three algorithms: PCA, PCA+LDA, BIC. Cumulative match curve and probe gallery permutation tools. Scripts for common experiments, including standard FERET. Supported platforms include Code is ANSI C: Unix, Windows, Turn-key scripts and code tested on Linux, Solaris, Darwin. Over 1,500 downloads since October 31, Related papers on web site. Near Future - Release: 5.0 Elastic Bunch Graph Matching (USC FERET). Data Preparation for Generalized Linear Models. PCA+LDA Configuration and FERET Subject Covariate Study. The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 20

21 The End The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 21

22 Help for csupreprocesnormalize The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 22

23 Help for SubspaceTrain The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 23

24 Help for csusubspaceproject The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 24

25 Help for csumakediffs First step in Bayesian Algorithm The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 25

26 Help for csubayesianproject The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 26

27 Help for csuanalysis Tools The CSU Face Identification Evaluation System, ICVS 2003 Talk Page 27