Forensics: The Next Frontier for Biometrics. Anil K. Jain Michigan State University October 27, 2015

Transcription

1 Forensics: The Next Frontier for Biometrics Anil K. Jain Michigan State University October 27, 2015

2 Bertillon System (1882) Habitual Criminal Act (1869): Identify repeat offenders H.T. F. Rhodes, Alphonse Bertillon: Father of Scientific Detection, Harrap, 1956

3 Friction Ridge Patterns Cummins and Midlo, Finger Prints, Palms and Soles, Dover,

4 Fingerprint Matching: 1992 v Apple iphone6 Sun SPARCstation host ~100 MHz CPU; 512 MB RAM 16 Xilinx 4010s as PEs (512 KB memory) CPU: 1.4 GHz RAM: 1GB On Splash MHz clock: ~ 6,300 matches/sec; on host 70 matches/sec. N. Ratha, D. Rover and A.K. Jain, "An FPGA-Based Point pattern Matching Processor with application to Fingerprint Matching", CAMP `95, Italy

5 Forensics & Biometrics: Shared Goals Latent prints Fibers Explosives Paint chips DNA Tire marks Shoe prints Bite marks SMT Forensics Biometrics Forensics: Identify suspects from crime scene evidence Biometrics: Automated person recognition from body traits 2D Face 3D Face Fingerprint Iris Speech Signature Gait Ear Palmprint Keystroke

6 Mugshots v. Body cam Images Constrained & cooperative Unconstrained & uncooperative 6

7 Biometrics v. Forensics Biometrics Forensics Subject Cooperative Uncooperative Data acquisition Constrained Unconstrained Data noise (Illumination, background, distortion) Low High Repeat acquisition Possible Not possible Data for training & test Moderate to large Small Purpose User authentication Convince jury & judge Statistical validation of identification Not required Necessary for conviction

8 Biometric Authentication People can no longer be trusted based on credentials (pw, PIN, ID card) Apple Pay: mobile payment (reader, template & matcher stored in phone) Meijer supermarket, Okemos U.S. Visit (OBIM) Cashless payment for lunch Time & Attendance UAE border crossing UAE border crossing HK smartcard ID Apple Pay Coal mine safety

9 World s Largest Biometric ID System ~900M unique 12-digit IDs issued (Oct. 2015) out of 1.2B residents 1: 900M slap & iris comparisons (de-dup) done before issuing new ID 9

10 Forensics: Latent Search Reference database Latent with markup Suspect may not be in the database Poor latent quality Errors in markup AFIS Scores: 1, Top 5 candidates

11 Forensics: Partial Face Search 80M Face Database Automated Face Search Who is he? One of them? Manual Forensic Examination D. Wang, C. Otto and A. K. Jain, "Face Search at Scale: 80 Million Gallery", MSU Technical Report, MSU-CSE-15-11, July 24, 2015

12 State of the Art: Fingerprint Matching Test Database & Evaluation Performance FpVTE 2003 FpVTE 2012 ELFT-EFS 2011 ELFT-EFS K plain fingerprints; 1:1 comparison (Medium scale) 30K subjects (slap) v. 100K subjects (slaps); 1:N comparison (open set) Latent identification 1,114 latent prints against 100K subjects (Rolled + Plain) 1,066 latent prints v. 100K subjects (Rolled + Plain) FRR = (right index finger) Rank-1: 62.2% Rank-1: 67.2%

13 State of the Art: Face Verification NIST FRGC v2.0 (2006) NIST MBGC (2010) LFW (2007) NIST IJB-A (2015) D. Wang, C. Otto and A. K. Jain, "Face Search at Scale: 80 Million Gallery", arxiv, July 28, 2015

14 Difficulties For Recognition Systems & Modeling Intra-class variation Inter-class similarity VID VEO NV Poor Latent quality Unconstrained face images

15 Biometrics to Forensics Improving latent performance Unconstrained face recognition Systems for tattoo matching, sketch (composite) to photo matching, altered fingerprint detection Addressing fundamental premise Distinctiveness (Pankanti, Prabhakar & Jain, 2002) Evidential value (Nagar, Choi & Jain, 2012) Persistence (Yoon & Jain, 2015) 15

16 Dictionary Learning Learning patches coarse-level dictionary (1,024 elements) Fingerprint database (NIST 4) (0, π/16] (π/16, 2π/16] patches (15π/16, π] 16 fine-level dictionaries (64 elements) 16

17 Latent Enhancement & Cropping Good quality Bad quality Ugly quality Latent Texture component Cropping Cropping & enhancement

18 Forensic Triage... Pool of examiners Sufficient quality? Yes Markups AFIS Yes Is markup needed? Latent No No Reject Rank-1 Rank-2... Top-K candidates & scores S. S. Arora, K. Cao, A. K. Jain and G. Michaud, "Crowd Powered Latent Fingerprint Identification: Fusing AFIS with Examiner Markups", ICB 2015

19 Variability in Markups Markup 1 (Rank 80) Markup 2 (Failed to match) Markup 3 (Rank 45) Lights-out: No match Fusion rank: 2 Markup 4 (Rank 7) Markup 5 (Rank 57) 258 latents 250K reference prints COTS AFIS 6 latent examiners Markup 6 (Rank 12,971) 19

20 How Many Markups? 107 latents did not need any markup; hit rate saturates with 3 examiners 20

21 Unconstrained Face Recognition: Landmarks Face detection and Landmarks (NIST IJB-A)

22 Unconstrained Face Recognition: Deep Learning # Parameters=5M Input face image Learning network parameters Learned representation Networks need large training set: 500K faces of 10K persons (CASIA)

23 Scars, Mark & Tattoos

24 J-E. Lee, W. Tong, R. Jin, and A. K. Jain, "Image Retrieval in Forensics: Tattoo Image Database Application", IEEE Multimedia, Vol. 19, 2012

25 Witness Description of Suspect Tipsters told police the gunman appears to be a man in his 30s with closecropped hair and stubble on his cheeks and chin

26 FaceSketchID System S. Klum, H. Han, B. Klare and A. K. Jain, "The FaceSketchID System: Matching Facial Composites to Mugshots", IEEE TIFS, 2014

27 Normal or Abnormal Ridge Flow? Fingerprint of Gus Winkler (1933) before and after alteration 27

28 Fingerprint Capture of Newborn & Infants September 21, 2015, Agra, India

29 Fingerprints of Newborns 6 hours (Subject 1) 1 week (Subject 2) 1270 ppi reader (2cm x 2cm x 8mm)

30 Summary Prob. models Domain experts Evidence Forensics Pattern recog. Data collection Image process.

31 What About the Data?