Serrurier Marseille E-books > International > Download e-book for iPad: Audio- and Video-Based Biometric Person Authentication: 4th by A.N. Rajagopalan, Rama Chellappa, Nathan Koterba (auth.),

Download e-book for iPad: Audio- and Video-Based Biometric Person Authentication: 4th by A.N. Rajagopalan, Rama Chellappa, Nathan Koterba (auth.),

By A.N. Rajagopalan, Rama Chellappa, Nathan Koterba (auth.), Josef Kittler, Mark S. Nixon (eds.)

ISBN-10: 3540403027

ISBN-13: 9783540403029

ISBN-10: 354044887X

ISBN-13: 9783540448877

This publication constitutes the refereed lawsuits of the 4th foreign convention on Audio-and Video-Based Biometric individual Authentication, AVBPA 2003, held in Guilford, united kingdom in June 2003.

The 39 revised complete plenary papers and seventy two revised complete poster papers have been rigorously reviewed and chosen for presentation. There are topical sections on face; speech; fingerprint; image,video processing, and monitoring; normal matters; handwriting, signature, and palm; gait; and fusion

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Read Online or Download Audio- and Video-Based Biometric Person Authentication: 4th International Conference, AVBPA 2003 Guildford, UK, June 9–11, 2003 Proceedings PDF

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Additional info for Audio- and Video-Based Biometric Person Authentication: 4th International Conference, AVBPA 2003 Guildford, UK, June 9–11, 2003 Proceedings

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Papageorgiou, and T. Poggio. Example-based object detection in images by components. In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 23, pages 349–361, April 2001. 27 [6] A. V. Nefian and M. H. Hayes. An embedded HMM-based approach for face detection and recognition. In Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 6, pages 3553–3556, 1999. 27 [7] H. Schneiderman and T. Kanade. A statistical method for 3D object detection applied to faces and cars.

Mouth Mouth expert: expert: Linear SVM Linear SVM 1. Shift 58x58 window over input image 2. Shift component experts over 58x58 window * *Outputs of component experts: bright intensities indicate high confidence. , O14 , X 14 , Y14 ) * (Ok , X k , Yk ) Combination Combination classifier: classifier: Linear SVM SVM Linear * (O14 , X 14 , Y14 ) 3. For each component k, determine its maximum output within a search region and its location: (Ok , X k , Yk ) 4. Final decision: face / background Fig.

ROC curves for the component-based and the global face recognition systems. Both systems were trained and tested on the same data This large discrepancy in results can be attributed to two main factors: First, the components of a face vary less under rotation than the whole face pattern, explaining why the component-based recognition is more robust against pose changes. Second, in contrast to the training data, the backgrounds in the test images were non-uniform. Component-based recognition only used face parts as input features for the classifier while the input features to the global system occasionally contained distracting background parts.

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Audio- and Video-Based Biometric Person Authentication: 4th International Conference, AVBPA 2003 Guildford, UK, June 9–11, 2003 Proceedings by A.N. Rajagopalan, Rama Chellappa, Nathan Koterba (auth.), Josef Kittler, Mark S. Nixon (eds.)


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