Benchmarking Human Ability to Recognize Faces & People
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1 Benchmarking Human Ability to Recognize Faces & People Dr. P. Jonathon Phillips National Institute of Standards and Technology 1
2 Who is this person?
3 Is this same person?
4 Unfamiliar Faces: How many identities here? Jenkins et al. (2011)
5 Key Papers P. J. Phillips and A. J. O Toole, Comparison of Human and Computer Performance Across Face Recognition Experiments, Image and Vision Computing, 32, 74-85, 2014 A. Rice, P. J. Phillips, V. Natu, X. An, and A. J. O Toole, Unaware Person Recognition from the Body when Face Identification Fails, Psychological Science, 24 (11), ,
6 Two Dimensions of Recognition Difficulty of Images Digital Point & Shoot Camera Mugshots Human Ability Low aptitude Super recognizer Super matcher
7 Measuring Human Performance Human subject raters respond 1. sure they are the same person 2. think they are the same person 3. not sure 4. think they are not the same person 5. sure they are not the same person
8 Area Under Curve (AUC) 8
9 The Good, Bad, & Ugly Face Challenge Three performance levels Good Bad Ugly Nikon D70-6 Mpixels (SLR) Indoor & outdoor images Frontal face images Taken within one year
10 Face Pairs Good Challenging Very Challenging
11 Face Pairs Good Challenging Very Challenging
12 Good, Bad, Ugly Performance
13 Area Under the ROC (AUC) Frontal Still Face Performance Human Algorithm FRGC Easy FRGC Difficult FRVT 2006 ND FRVT 2006 Sandia Ugly Bad Good 13
14 Is this same person?
15 Is this same person?
16 Is this same person?
17 Human Performance on Hard Face-Pairs Algorithm Face only Original image Face masked 17
18 Rated Use of Internal and External Features More Use Less Use
19 Example of Point & Shoot Face Images Courtesy PittPatt
20 Range of Performance Verification Rate at FAR = MBE 2010 GBU 2011 LFW 2012 P&SC 2012 Mugshots Digital SLR Web Photos Digital Point & Shoot Cameras 20
21 Glasgow Face Matching Test Same or different? Burton, White & McNeill (2010). Behavior Research Methods, 42,
22 Glasgow Face Matching Test Burton, White & McNeill (2010). Behavior Research Methods, 42,
23 Video: Walking vs. Conversation Human subject raters respond 1. sure they are the same person 2. think they are the same person 3. not sure 4. think they are not the same person 5. sure they are not the same person
24 Gait Experiments gait video Static Face GG conversation video CG body only face only CC
25 Human and Machine Performance For frontal, machine and human performance related Algorithms Better (Untrained Humans) Mugshots & Mobile Studio environments Digital Single Lens Reflex Mobile Studio and Ambient Lighting Humans Better Non-face identity cues Cross-pose (video one experiment) Not Measured Point and Shot Cameras Change in Pose (in general)
26 Questions? 26
27 Algorithm AUC Hurdle: Measuring Success Develop structure for comparing human and machine performance Human AUC Adapting recent methods from Neuroscience. 27
28 Hurdle: Measuring Success 28
29 Hurdle: Measuring Success 29
30 The Challenge Problem: Robust Recognition of Unfamiliar Faces Goal: Human Level Performance Untrained Humans Trained Professionals Forensic Examiners Compare Machine & Human on a Face Performance Index Objective: Move Machine Performance into the Goal Box
31 Robust Face Recognition
32 Video: Walking vs. Walking Human subject raters respond 1. sure they are the same person 2. think they are the same person 3. not sure 4. think they are not the same person 5. sure they are not the same person
33 Area Under the ROC (ROC) Frontal Still Face Performance Human 0.70 Human FRGC Easy FRGC Difficult FRVT 2006 ND FRVT 2006 Sandia Ugly Bad Good 33
34 Human and Machine Performance Mugshots & Mobile Studio environments FRVT 2002/2006 MBE 2010 Mobile Studio vs Ambient Lighting FRGC FRVT 2006 Ambient Lighting (indoor/outdoor) Good, Bad, & Ugly Hard Still Cases (reverse ROC) Video
35 Next Directions In hard cases (poor viewing conditions), humans take advantage of face, body, still, & video Evidence: algorithms do NOT take advantage of face, body, still, & video Learn from the human visual system. Functional Perceptual Incorporate into algorithm design.
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