Facial Recognition in Biometrics

1. Susan Simmons University of North Carolina Wilmington Facial Recognition in Biometrics

2. Biometrics • Biometrics (wikipedia) -- Biometrics are used to identify the identity of an input sample when compared to a template, used in cases to identify specific people by certain characteristics. • possession-based: using one specific "token" such as a security tag or a card • knowledge-based: the use of a code or password. • Biometric – physical or behavioral characteristic • Biometrics (questbiometric.com) -- The word "biometrics" is derived from the Greek words 'bios' and 'metric' ; which means life and measurement respectively. This directly translates into "life measurement”. General science has included biometrics as a field of statistical development since the early twentieth century. • Biometrics technologies measure a particular set of a person's vital statistics in order to determine identity. Biometrics in the high technology sector refers to a particular class of identification technologies. These technologies use an individual's unique biological traits to determine one's identity or to verify one’s identity.The traits that are considered include fingerprints, retina and iris patterns, facial characteristics and many more.

3. A little history

4. Examples

5. We see biometrics in many different places today. • Voter Registration • Driver Licensing • Border Control • Passport / VISA • Criminal ID / Wanted Persons Lookup • Airports / Frequent Traveler / Passenger Tracking

6. Facial recognition • Can be used for surveillance • Find criminals, terrorists, missing children • Involves non-invasive, contact-free process • Can be integrated with existing surveillance systems • Marketing • Identify demographics interested in products • Some examples • http://www.youtube.com/watch?v=H2a0KYtG97E • http://www.youtube.com/swf/l.swf?video_id=jADItDHOHOA

7. Problems in Facial recognition • Privacy issues facial recognition • Violation of people’s privacy? • Right to search database for match of images captured in public surveillance cameras? • Uncontrolled background (including lighting, shadows, glares) • Camera angle • Image resolution • Part of face hidden (sunglasses, hat, profile, etc)

8. Identify facial images Need to identify facial images from a video or picture

9. Eigenfaces • Take an N x N image and convert it to an N2 x 1 vector • Use a subset of the face images as a training set (each face must be centered and of the same size) • Calculate the eigenvectors of the covariance matrix of the images, keeping on K eigenvectors (corresponding to the larges K eigenvalues) • Uses of eigenfaces • After centering new images, calculate the distance between the new image and all images in database. If distance is less than a set cutoff, then the picture is recognized as that face. • Can also do this same exercise to determine if an image is a face.

10. Active Appearance Models The following approach works well with facial images that are forwarding facing and not much facial expression. The shape of a face is found using Active Shape Models (ASM). Identifies the outline of the face as well as important landmarks on the face.

11. Sean Connery (1959)

12. Modeling shape in AAMs The algorithm uses a subset of images to train the model. Points are aligned into a common co-ordinate frame and represented by a vector x (x = (x1, x2,x3…, y1, y2, y3,…)T). Principal Component Analysis (PCA) is then applied to the data

13. Modeling grey-level appearance Each image is warped so that its control points match the mean shape (using a triangulation algorithm) To minimize the effect of global lighting variation, we normalize the example samples by applying scaling and offset PCA is applied to the normalized data

14. Using the models to vary parameters

15. Movie Varying the first parameter Varying the seventh parameter

16. Age estimation Random Forest (by Leo Brieman) uses decision trees to estimate age. Support Vector Regression – (input info about support vector regression) MAE % error w/+5 Random Forest 12.04713 0.1722222 Support Vector Machine 9.254012 0.3555556

17. Aging faces

18. Current methodology • Use Monte Carlo simulation to simulate potential b vectors • Use a classification method from the model to estimate age (for example, support vector regression or random forest, etc) • Create a look-up table for the average b-vectors • Use the look-up values to age individuals (use their b-vectors)

19. An example A hypothetical “b-vector look-up table with the dimension of b = 4 AGE 2.130630 6.953627 -1.232983 2.723359 20 -0.9059754 0.5859145 -4.1489520 0.5211284 25 5.6084822 -0.3766133 3.4587549 9.8395541 30 A new individual’s “b-vector” at age 20 1.6752062 0.2979551 3.8977920 5.1741853 To age this individual to 30, we need to shift their b-values 1.6752062 + (5.6084822 - 2.130630) = 5.153058 0.2979551 + (-0.3766133 - 6.953627) = -7.032285 3.8977920 +(3.4587549 – (-1.232983 )) = 8.58953 5.1741853 + (9.8395541 - 2.723359) = 12.29038

20. Some examples

21. Conclusions • Biometrics continuously receives more attention • Need to create database • Much more work to be done by MANY different fields

22. Special Thanks to • Ms. Amrutha Sethuram • Drs. Karl Ricanek (Computer Science), Yishi Wang (Statistics) • Mr. Fernando Schiefelbein (graduate student), Mr. Philip Whisenhurst (undergraduate student)