The need for accurate and unforgeable identity recognition techniques has become an issue of increasing urgency. Biometric approaches such as iris recognition hold huge promise but still have significant limitations, including susceptibility to 'spoofing'. This project seeks to advance our knowledge of security and accuracy of multibiometric systems by inventing, evaluating, and applying innovative methods and tools to combine highly accurate static traits, such as iris patterns, with novel traits based on the dynamics of eye movements. The strategy is to use existing iris recognition hardware to combine three different biometrics approaches related to the eye: measurement of iris patterns, unique characteristics of the eye globe and its muscles, and the brain's strategies for guiding visual attention. This multimodal ocular biometrics approach has the potential to improve liveness detection and resistance to sophisticated counterfeiting techniques and coercion attacks, while improving identification accuracy. This research tackles important questions related to the individuality, variability, scalability, and longevity of these ocular traits, building a foundation for security and accuracy improvement when those traits are combined with iris recognition. This project aims to benefit efforts such as the Unique Identification project in India, which seeks to use biometric information of 1.2 billion individuals to fight fraud.
Educational activities include three initiatives: 1) creation of a strong outreach activity to K-12 students, 2) expansion of an interdisciplinary research-oriented educational program previously created by the PI for undergraduate and graduate students, and 3) mentoring and guidance to interest undergraduate students in scientific careers and encourage more students from diverse backgrounds to pursue graduate study.
