In this paper we propose a methodology for selecting the most discriminative features in a set for online signature verification. We expose the difference in the definition of class between signature verification and other pattern recognition tasks, and extend the classical Fisher ratio to make it more robust to the small sample sizes typically found when dealing with global features and client enrollment time constraints for signature verification systems. We apply our methodology to global and local features extracted from a 50-users database, and find that our criterion agrees better with classifier error rates for local features than for global features. We discuss the possibility of performing feature selection without having forgery data available.
Models for predicting judgments about the quality of Spoken Dialog Systems have been used as overall evaluation metric or as optimization functions in adaptive systems. We describe a new approach to such models, using Hidden Markov Models (HMMs). The user's opinion is regarded as a continuous process evolving over time. We present the data collection method and results achieved with the HMM model.
We present a new technique based on using embedded compass (magnetic) sensor for efficient use of 3D space around a mobile device for interaction with the device. Around Device Interaction (ADI) enables extending interaction space of small mobile and tangible devices beyond their physical boundary. Our proposed method is based on using compass (magnetic field) sensor integrated in new mobile devices (e.g. iPhone 3GS, G1/2 Android). In this method, a properly shaped permanent magnet (e.g. a rod, pen or a ring) is used for interaction. The user makes coarse gestures in 3D space around the device using the magnet. Movement of the magnet affects magnetic field sensed by the compass sensor integrated in the device. The temporal pattern of the gesture is then used as a basis for sending different interaction commands to the mobile device. The proposed method does not impose changes in hardware and physical specifications of the mobile device, and unlike optical methods is not limited by occlusion problems. Therefore, it allows for efficient use of 3D space around device, including back of device. Zooming, turning pages, accepting/rejecting calls, clicking items, controlling a music player, and mobile game interaction are some example use cases. Initial evaluation of our algorithm using a prototype application developed for iPhone shows convincing gesture classification results.
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