Researcher: Vijayakumar Bhagavatula
Research Area: Security of Cyber-Physical Systems
Cross Cutting Thrusts: Cryptography
The user name/password paradigm is the most commonly used authentication mechanism for access physical devices and space. However, passwords have well-known limitations - they are often not as strong as they need to be and can be compromised via dictionary attacks. One way to improve the security is by using biometric signatures (e.g. face image, iris image, etc.) to verify the identity of a claimant. The main challenge in biometric recognition is the inherent variability in biometric signatures - for example, the appearance of a face image can change significantly with illuminations, pose, expressions, etc.
We have developed correlation filter methods to reduce the error rates in the presence of such variability. A more common mechanism for authenticating a user is through cryptographic keys. While such cryptographic keys exhibit sufficient entropy,because of their inherent randomness, they are usually stored in and retrieved from a physical medium such as a smart card, most often using a password. Thus, they are only as secure as the password being used. One way to combine the strengths of biometrics and cryptography is to generate encryption keys from biometric signatures.
We propose that current methods of biometric encryption are inadequate in one of the three desired attributes: 1) long encryption keys 2) low false reject rates and false accept rates and 3) tolerance to normal distortions such as de-centering, small rotations, expression changes, etc. We believe that he performance of biometric encryption schemes can be improved by using advanced correlation filters in place of conventional methods, e.g. those based on minutiae (for fingerprints) or iris codes (for iris images.) Correlation filters offer advantages of shift-invariance, graceful degradation and distortion tolerance, which should prove very useful in handling the normal variability anticipated in biometric signatures. The goal of this project is to develop and evaluate methods to apply advance correlation filters to improve the capabilities (quantified by encryption key length, false reject rate and false accept rate) of biometric encryption.