Future of Biometrics

1. Future of Biometrics Marina Gavrilova

2. Biometric model validation

3. Biometric data model validation Scientific question: If a person's photo in the system's database was taken 10 years ago, is it possible to identify the person today? Answer can be provided by next generation face reconstruction engines.

4. Biometric fusion

5. Fusion • Biometric Fusion means integration of biometric information. • The goal of fusion scheme is to devise an appropriate function that can optimally combine the information rendered by the biometric subsystems. • Broadly classified as – Prior-to-matching fusion and After-matching fusion

6. Biometric and robotics

7. Humanoid robots Humanoid robots are anthropomorphic robots (have human-like shape) that include also human-like behavioral traits. The field of humanoid robotics includes various challenging direct and inverse biometrics. On the other hand, in relation to inverse biometrics, robots attempt to generate postures, poses, face expressions to better communicate their human masters (or to each other) the internal states [49]). Robots such as Kismet express calm, interest, disgust, happiness, surprise, etc (see (MIT projects). More advanced aspects include dialogue and logical reasoning similar to those of humans. As more robots would enter our society it will become useful to distinguish them among each other by robotic biometrics.

8. Robot reasoning More advanced aspects include dialogue and logical reasoning similar to those of humans. As more robots would enter our society it will become useful to distinguish them among each other by robotic biometrics. Asimo (Honda) humanoid robot http://en.wikipedia.org/wiki/ASIMO

9. Biometric Privacy and Security

17. Secure Email with Fingerprint Recognition 17

18. Email Changed the way we communicate in today’s highly technical world What's wrong with the app of the Internet? Hard to know who sent an email Spam Unsolicited email Offensive Fraudulent (phishing) Malicious (viruses, worms, spyware, exploits, DoS) 18

19. Related work Email (E) If H`(E) = H(E) then message is authentic Hash H (MD5, SHA-1, etc) H`(E) H(E) Private key D Signing Algorithm Public key E Signature (D(H(E))) D(H(E)) Hash H E 19

20. Some works on this field PEM (Privacy Enhanced Mail) [3] – mid 1980 ASCII email messages No centralized public key directory Single root to issue CAs (Certificate Authority) S/MIME (Secure Multipurpose Internet Mail Extension) [4] Accommodate any number of trusted CAs PGP (Pretty Good Privacy) [5] Web of trust Widely used 20

21. Some works on this field Garfinkel [6] presented a new approach to solve most of the usability issues Used only for encryption Outgoing email Looks up users public keys in a local database Appends the user’s public key to the email header Incoming email Stores public keys found in the email header Vulnerable to man-in-the-middle attacks 21

22. Some works on this field Brown and Snow [7] presented a similar approach but adds digital signatures Proxy-based approach sitting between the mail client and mail server Encrypts and signs all outgoing emails Decrypts and verifies all incoming emails 22

23. Another approach Idea Use fingerprints instead of private keys Primary goals Secure access to email accounts Provide an easier way to sign and verify emails Solve the usability issues Implemented as an email client called SEFR SEFR asks you to present your fingerprint When you access it and try to view your inbox When you try to send an email 23

24. Another approach Components Database: used to store user’s fingerprints and account information dbs2.cpsc.ucalgary.ca Enroller: used to enroll new users Receiver: used to download the user’s inbox Using POP (Post Office Protocol) [1] Gmail’s POP server – pop.gmail.com Port 995 Sender:used to send emails Using SMTP (Simple Mail Transfer Protocol) [2] Gmail’s SMTP server – smtp.gmail.com Port 465 24

25. Another approach Accounts on Gmail Two accounts were created for testing and experimentation purposes amaobied sefr.obied Issues Gmail servers requires the use of SSL OpenSSL Base 64 encoding Fingerprint scanner in the BT lab No API Used fingerprint image paths 25

26. Another approach Signing messages When a user tries to send an email, SEFR asks the user to present his/her fingerprint. If the fingerprint is stored in the database, SEFR does the following: Transforms the email message (e.g., get rid of newlines, tabs, spaces, etc) Create a hash using SHA-1 of the transformed message Store the sender’s email address, receiver’s email address and hash in the database 26

27. Another approach Verifying messages When SEFR tries to verify an email, SEFR automatically does the following: Transforms the messages (e.g., get rid of newlines, tabs, spaces, etc) Creates a hash using SHA-1 of the transformed message Extracts the sender’s email address, receiver’s email address from the email header Checks if the sender’s email address is associated with the receiver’s email address and hash value in the database 27

28. Future Research Direction Using biometric authentication to access Web-based system Online banking Defeating Spam Bill Gates said “Two years from now, spam will be solved” 28

29. Cancelable biometrics

30. Cancelable biometrics The issue of protecting privacy in biometric systems has inspired the area of so-called cancelable biometrics. It was first initiated by The Exploratory Computer Vision Group at IBM T.J. Watson Research Center and published in [2]. Cancelable biometrics aim to enhance the security and privacy of biometric authentication through generation of “deformed“ biometric data, i.e. synthetic biometrics. Instead of using a true object (finger, face), the fingerprint or face image is intentionally distorted in a repeatable manner, and this new print or image is used.

31. Biometric encription

37. Biometric lie detectors

38. Synthetic biometric data in the development of a new generation of lie detectors The features of the new generation of lie detectors include: (a) Architectural characteristics (highly parallel configuration), (b) Artificial intelligence support of decision making, and (c) New paradigms (non-contact testing scenario, controlled dialogue scenarios, flexible source use, and the possibility of interaction through an artificial intelligence supported machine-human interface).

39. Synthetic biometric data in early warning and detection system design The idea of modeling biometric data for decision making support enhancement at checkpoints is explored, in particular, at the Biometric Technologies Laboratory at the University of Calgary (http://enel.btlab.ucalgary.ca). Simulators of biometric data are emerging technologies for educational and training purposes (immigration control, banking service, police, justice, etc.). They emphasize decision-making skills in non-standard and extreme situations.

40. Thank You