Behavioral biometrics, such as gait, walking trajectory, and speech rhythm, are advantageous in their tolerance of low sensory resolution, long sensing distance, and poor environmental conditions. Many applications in security and tele-healthcare require tracking and identification of individuals based on their behavioral traits. Conventional approaches typically use video cameras, which could incur high data throughput and computational costs. This project aims to develop a research infrastructure that enables tracking and identification of individuals? behavioral biometrics through thermal, pressure, photonic, acoustic, fiber-optic, and laser sensors. Besides low cost, those sensors can produce sparse behavioral biometric data that can be analyzed quickly. To achieve this goal, we will develop: (1) a set of tools for novel sampling geometries, cognitive sensing protocols, biometric feature modeling and distributed information aggregation; (2) a multi-agent architecture for system operation and adaptation that enables heterogeneous sensors to achieve consensus in behavior analysis; and (3) a set of programming and evaluation tools for system integration and improvement. The developed computing research infrastructure will enable research activities about behavioral biometrics based on low-cost, low-power, distributed platforms with a variety of sensing modalities. The enabled distributed behavioral biometrics intelligence has great potential to cope with the technical challenges in behavioral biometrics caused by long distances and crowded scenes. The developed education materials will benefit a number of computing and engineering courses for the training of highly skilled workforce on surveillance and security monitoring. The outreach programs include summer student training, faculty workshops, and school visits.
