1. Biometrics W. A. Barrett, cmpe dept., SJSU vs. 2.0 ● Theory in a nutshell ● Segmentation ● Recognition ● Verification ● Fingerprints/Face/Iris/Speaker recognition ● Logface matching

2. Theory in a Nutshell ● Capture images of objects (usually persons) ● Segment a view ● Compress views to biometric codes. ● Compare two biometric codes, yielding a biometric difference. ● When two differences are small enough (less than some threshold), the corresponding objects are considered the same. ● Otherwise the objects are considered different.

3. A Sample Database distance j =  square(D[j,i] - C[i])/var[i] where D[j,i] = database j’s component i, 1 <= j <= 5 (rows) C[i] = candidate component i, 1 <= i <= 6 (columns) var[i]= variance from database, component i

4. Distance Calculation (see spreadsheet local.xls or web.xlslocal.xlsweb.xls

5. Segmentation ● Image typically contains background noise ● Segmentation is isolating a biometric view from the image – Motion segmentation uses video to reject static background pixels – Two or more cameras yield distance measures – Given a static image, segmentation requires heuristic methods ● Static segmentation may be the most difficult design challenge of a biometric system

6. Recognition ● Form an enrolled database of biometric codes – each entry represents a different candidate – each candidate is associated with a biometric code, name, address, etc. ● Capture a view of a candidate and compute its biometric code C. ● Compare C with all candidates in the database. ● Form a list of database candidates, ordered by increasing biometric distance. ● Front of the list should be the matching candidates.

7. Recognition (2) ● If the top candidate has a small-enough biometric distance, we say that we have recognized the candidate. ● If the top candidate's biometric distance is too large, then the candidate has not been recognized. ● This implies a threshold level has been determined for biometric differences

8. Recognition -- Four Cases ● (good) Top candidate's biocode is small enough, and is the correct person. ● (bad) Top candidate's biocode is small enough, but is the wrong person (false acceptance) ● (good) Top candidate's biocode is too large, and this is the wrong person. ● (bad) Top candidate's biocode is too large, yet this is the correct person (false rejection)

9. Recognition Goals ● Maximize correct matching of a candidate to the database ● Minimize false acceptance and false recognition

10. Verification ● Candidate presents biometric image PLUS identification information, such as a credit card plus PIN ● System locates candidate in the database through the credit card/PIN data ● One biometric distance is computed -- if small enough, the candidate is verified. ● Can still have a false acceptance or false rejection!

11. Authentics-Imposters ● Biometric quality is measured statistically by acquiring two distributions -- ● Authentics -- distribution of biometric distances of the same persons, but with different images ● Imposters -- distribution of biometric distances of images of pairs of different persons ● These should be widely separated, but often aren't

12. Authentics - Imposters

13. ● The two distributions will overlap in general ● The extent of the overlap relative to the two areas provides a measure of the quality of this biometric measure ● Small overlap -- good biometric ● Large overlap -- poor biometric ● Best viewed through the accumulated distribution – shows probability of correct identification ● See spreadsheet local.xls or web.xls for a modellocal.xls web.xls

14. Authentics-Imposters

15. Choice of Threshold ● At the crossover of the A-I curves, we have a threshold that makes false acceptance rate == false rejection rate ● Assumes that the relative number of attempts is balanced ● Moving the threshold to the left means more false rejections, but fewer false acceptances ● Moving the threshold to the right means fewer false rejections, more false acceptances

16. Quality Measure ● The quality of a biometric measure can be estimated from these two curves – use a good representative sample of measurements (not easily done!) – find the crossover point – FARR = % at crossover point ● FARR: False Acceptance-Recognition Ratio

17. View Compression ● Task: form a biometric code from a view – Fast Fourier transform – Gabor wavelet transform – Legendre moments – Chebyshev moments – pseudo-Zernike moments ● The choice should: – eliminate unwanted view variations (scale, rotation, translation, avg intensity, etc.) – produce maximum discrimination, i.e. smallest possible FARR

18. Legendre Moments f(x,y) is the image intensity vector P 0 (x) = 1, P 1 (x) = x

19. Legendre Moments ● Are orthogonal and complete – the view can be reconstructed, given enough (p,q) pairs ● Are translation invariant – the translation component is in (0,0) ● Are not scale invariant – face: need to rescale to a normal view, typically done by finding the eyes, etc. ● Are not rotation invariant – face: measure degrades with rotation

20. pseudo-Zernike Moments ● Much more complex set of polynomials ● Are orthogonal and complete ● Not scale or translation invariant ● Certain functions of the moments are rotation invariant – most human biometrics don't need this ● Used in advanced optical calculations ● Useful for logface biometrics

21. Face Recognition ● Many methods have been proposed – eigenfaces (Alex Pentland, MIT) – feature extraction (Joseph Attick, Identix) – some are proprietary ● Discrimination depends critically on – uniform lighting conditions – full frontal face -- no side views – “plain” expression – no attempt at disguise – good segmentation, centering the eyes ● Best results FARR = 1-5%

22. Face Recognition ● Relatively high FARR means restricted use: – verification under controlled conditions (disguise can be used to evade detection, but difficult to fake a verification trial) – sifting out a small number of candidates from a larger set ● NOT indicated for – recognition – critical applications

23. Fingerprints ● For digital prints, the FBI routinely finds persons in their large national database from prints sent through the internet (AFIS) ● Statistics are unknown, but believed to have a FARR less than 1 E-5 – Fingerprint analysis for forensic purposes has a much smaller FARR – Small or smudged prints (typical of crime scenes) are likely to result in identification errors.

24. Iris Scanning

25. ● Image capture requires telephoto camera – Daugman recommends infrared light ● Locate pupil (heuristic) – Daugman uses a circle-finding algorithm ● Locate sclera – surrounds pupil ● Locate upper and lower eyelids ● Form biocode from iris patterns – Daugman uses 8 bands and a Gabor filtering to yield a 256 byte code ● Distance measure – Daugman uses a Hamming distance measure

26. Iris Scan A-I distribution from John Daugman's patent

27. Iris Issues ● Pupil finding is difficult ● Background light sources reflected in pupil ● Eyelashes sometimes obscure iris ● Eyes may be partly closed ● Eye movements are rapid, may cause image capture failure ● Telephoto centering and autofocus important ● Capture system can be expensive – Sensar’s manufacturing cost ~$2,000 ● Recognition failure rate fairly large ~1-5%

28. Sensar, Inc. ● A New Jersey startup, 1990-2000 period ● Used the Daugman iris patent ● Developed extraordinary optics system – two cameras, one wide-angle, the other a telephoto with autofocus and angular tracking – system could accurately identify a person as he/she approached an ATM machine ● tested in a Fort Worth bank system ● Sensar failed for various reasons

29. Speaker Recognition ● Starts with an audio sample of a human voice ● Typically, person is prompted to repeat certain phrases ● Speech fragment compressed by FFT or wavelet transforms ● Identification/verification similar to other biometrics ● FARR ~ 1E-2 at best

30. Forest Service Project ● Goal -- Match a cut log face to its mating stump ● U. S. Forest Service interested in combating theft of timber from national forests – start with photo of stump face – find stump face in a collection of photographs of faces taken at various sawmills – use biometrics to filter out the most likely candidates – use forensic tools to indict and prosecute thieves

32. Logface System Features ● Color images input by digital camera, many supported image formats ● Semi-automatic segmentation of log faces – operator segmentation needed ● Uses pseudo-Zernike polynomials to obtain a rotation-invariant biometric code ● Database mysql employed under Linux ● Friendly user environment for locating matching faces from a database

33. Logface Results

34. Selected Bibliography http://www.biometrics.orghttp://www.biometrics.org -- Biometrics web site http://www.identix.comhttp://www.identix.com -- Face recognition, fingerprint vendor http://www.iritech.comhttp://www.iritech.com -- Daugman’s iris scanning company, patent holder John Daugman, patent no. 5,291,560, Iris scanning patent Wechsler et al, editors, Face Recognition Maltoni, Maio, Jain & Prabhakar, Handbook of Fingerprint Recognition, Springer, 2003. Mukundan & Ramakrishnan, Moment Functions in Image Analysis, World Scientific, 2003 Duda & Hart, Pattern Classification and Scene Analysis, Wiley Interscience Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press Theodoridis & Koutroumbas, Pattern Recognition, Academic Press

35. Summary ● Biometrics is an established discipline ●...though research is ongoing ● Mechanism is – compressing an image into a biocode – comparing pairs of biocodes with a distance measure d(I1, I2) – forming a database of enrollees – locating or verifying a candidate against the database with the distance measure

36. Summary ● FARR = equal false acceptance and false rejection ratio ● Most popular human biometrics – digital fingerprints, with FARR ~ 1E-5 – forensic fingerprints (non-digital), FARR < 1E-7 – face, with FARR ~ 1E-2 at best – iris, with FARR < 1E-7 – speaker recognition, with FARR < 1E-2 ● Other applications ● Draws upon pattern recognition theory