Biometric Intelligent Interfaces

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The proposed research program envisions building a unified framework for an ambient biometric sensing and identification environment. The framework will integrate emerging accurate and perceivably latent biometric sensing, intelligent image/signal processing, and advanced decision-making support. The framework will result in control interfaces that provide seamless scans of gestures, face and facial expression, and body, for identification and continuous tracking of a user's actions. This will enable the combination of the concept of Natural, Intuitive and Immersive (NII) interfaces, and the security of the access to the NIIs, through enhanced usage of biometric technologies. This will not only permit integration of multiple NII in security access control applications, but will also reinforce security and privacy of related NII applications, such as contactless interfaces for personal devices, computer systems, and situational awareness environments. The middleware of the framework will be built upon a platform that uses an Application Programming Interface, and performs local processing for low power, as well as sensor processing in parallel with application processing for high performance, using Graphic Processing Units. We will address this technology issues by integration of biometric sensors with intelligent context-aware support. This will involve multimodal data storage and processing using content-based multimodal data analysis and indexing. This will be addressed in this research program via syntactic and semantic analysis of multimodal input and multimedia data, in order to provide intelligent, or context-aware, retrieval of stored multimedia. The other challenge is a trade-off between low power/cost of implementation and low precision of the sensors. Ways to resolve this problem include using multi-sensor approach, as well as embedding more intelligence in sensors to compensate for quality. This proposal will deploy the most advanced RGB-Depth sensors, such as the multi-sensor approach currently embodied in PrimeSense’s Carmine, Microsoft's Kinect that perceives and identifies objects, face and full body motions using depth data from objects at 0.8 -3m distance, as well as the Leap 3D Motion control that allow recognition of users' finger gestures with superior precision (1/100 mm) at 0.2-0.8 m. The project will be implemented in the Biometric Technologies Laboratory at the University of Calgary, and is based on the previous and current projects, involving modeling of biometrics such as fingerprint, iris and face, facial expression recognition, face recognition in both visual and infrared spectra, as well as creating the prototyping modules for a new-generation of situational awareness biometric systems, based on multi-sensor and multi-modal structure and Bayesian decision-making. The ultimate vision of this research program is to bring this biometric platform to the level of NII that will influence all levels of the underlying technology, enable the anticipated applications (in human-personal device interaction, security, health care and education), and create a whole host of future applications waiting to be discovered.