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Pattern Recognition, Machine Intelligence and Biometrics | Patrick S. P. Wang | Springer

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform DWT with the local interactions of the facial structures expressed through the structural Authors: P. Nicholl, A. Amira, D. Bouchaffra and R. We present a system that simultaneously tracks eyes and detects eye blinks.

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First, in case of user with glasses, m Due to vast variations of extrinsic and intrinsic imaging conditions, face recognition remained to be a challenging computer vision problem even today. This is particularly true when the passive imaging approa Features extracted from electroencephalogram EEG recordings have proved to be unique enough between subjects for biometric applications.

We show here that biometry based on these recordings offers a novel wa Authors: A. Riera, A.

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Soria-Frisch, M. Caparrini, C. Grau and G. Face recognition under varying pose is a challenging problem, especially when illumination variations are also present.

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In this paper, we propose to address one of the most challenging scenarios in face recogn Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Search all SpringerOpen articles Search. A Novel Retinal Identification System This paper presents a novel biometric identification system with high performance based on the features obtained from human retinal images. Human Gait Recognition Based on Multiview Gait Sequences Most of the existing gait recognition methods rely on a single view, usually the side view, of the walking person.

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Biometric Methods for Secure Communications in Body Sensor Networks: Resource-Efficient Key Management and Signal-Level Data Scrambling As electronic communications become more prevalent, mobile and universal, the threats of data compromises also accordingly loom larger. In , the group introduced a novel texture-based approach for face description. The approach is based on the efficient local binary pattern LBP features developed by the group a few years ago. Excellent results were obtained in face detection, face recognition and facial expression recognition.

Spatiotemporal local binary patterns were later introduced and applied successfully to face analysis from video sequences for face and facial expression recognition, visual-speech recognition, lip reading, gender classification etc. Figure 1: The basic LBP operator. It forms labels for the image pixels by thresholding the 3x3 neighborhood with the center value and considering the result as a binary number. Our more recent work focused on applying manifold learning techniques to face analysis face recognition, age estimation, gender classification etc.