Biometrics Driven Smart Environments: Abstract Framework and Evaluation

Framework and Evaluation Vivek Menon, Bharat Jayaraman, and Venu Govindaraju 1 Amrita University, Coimbatore 641 105, India vivek [email protected] 2 University at Buffalo, Buffalo, NY 14260, USA [email protected], [email protected] Abstract. We present an abstract framework for ‘smart indoor environments’ that are monitored unobtrusively by biometrics capture devices, such as video cameras, microphones, etc. Our interest is in developing smart environments that keep track of their occupants and are capable of answering questions about the whereabouts of the occupants. We abstract the smart environment by a state transition system: Each state records a set of individuals who are present in various zones of the environment. Since biometric recognition is inexact, state information is probabilistic in nature. An event abstracts a biometric recognition step, and the transition function abstracts the reasoning necessary to effect state transitions. In this manner, we are able to accommodate different types of biometric sensors and also different criteria for state transitions. We define the notions of ‘precision’ and ‘recall’ of a smart environment in terms of how well it is capable of identifying occupants. We have developed a prototype smart environment based upon our proposed concepts, and provide experimental results in this paper. Our conclusion is that the state transition model is an effective abstraction of a smart environment and serves as a basis for integrating various recognition and reasoning capabilities. We present an abstract framework for ‘smart indoor environments’ that are monitored unobtrusively by biometrics capture devices, such as video cameras, microphones, etc. Our interest is in developing smart environments that keep track of their occupants and are capable of answering questions about the whereabouts of the occupants. We abstract the smart environment by a state transition system: Each state records a set of individuals who are present in various zones of the environment. Since biometric recognition is inexact, state information is probabilistic in nature. An event abstracts a biometric recognition step, and the transition function abstracts the reasoning necessary to effect state transitions. In this manner, we are able to accommodate different types of biometric sensors and also different criteria for state transitions. We define the notions of ‘precision’ and ‘recall’ of a smart environment in terms of how well it is capable of identifying occupants. We have developed a prototype smart environment based upon our proposed concepts, and provide experimental results in this paper. Our conclusion is that the state transition model is an effective abstraction of a smart environment and serves as a basis for integrating various recognition and reasoning capabilities.