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Download Audio- and Video-Based Biometric Person Authentication: by J. Kittler, R. Ghaderi, T. Windeatt, J. Matas (auth.), Josef PDF

By J. Kittler, R. Ghaderi, T. Windeatt, J. Matas (auth.), Josef Bigun, Fabrizio Smeraldi (eds.)

This e-book constitutes the refereed lawsuits of the 3rd overseas convention on Audio- and Video-Based Biometric individual Authentication, AVBPA 2001, held in Halmstad, Sweden in June 2001.
The fifty one revised papers offered including 3 invited papers have been conscientiously reviewed and chosen for inclusion within the ebook. The papers are equipped in topical sections on face as biometrics; face picture processing; speech as biometrics and speech processing; fingerprints as biometrics; gait as biometrics; and hand, signature, and iris as biometrics.

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In Section 3, based on the integration of shape and texture features after PCA and a similarity function, the approach for face identification is developed. The efficiency of our algorithm is verified in Section 4 through 3 test cases. 1 Localization of Feature Points Definition of Feature Points In our system, two types of feature points are defined for recognition. One type is in 3D and is described by the Point Signature, while the other is in 2D and is represented by Gabor wavelets. To choose these feature points, computation requirements, distinction, representative and the insensitivity to the expression and viewpoint variations are the main concerns that are considered.

Konen, “Distortion invariant object recognition in the dynamic link architecture,” IEEE Trans. Computers, vol. 42, pp. 300–311, 1993. 12. C. Liu and H. Wechsler, “Comparative assessment of independent component analysis (ICA) for face recognition,” in Proc. Second International Conference on Audio- and Video-based Biometric Person Authentication, Washington D. , March 22-24, 1999. 13. C. Liu and H. Wechsler, “Robust coding schemes for indexing and retrieval from large face databases,” IEEE Trans.

Proc. of FGR, pp. 208-213, 2000. Y. Li, S. Gong, and H. Liddell. Support Vector Regression and Classification based MultiView Face Detection and Recognition. Proc. of FGR, pp. 300-305, 2000. B. Moghaddam, M. Yang. Gender Classification with Support Vector Machines. Proc. of FGR, pp. 306-311, 2000. P. Phillips. Support Vector Machines Applied to Face Recognition. Technical Report, NIST, 1999. H. Vafaie and K. DeJong. Feature Space Transformation Using Genetic Algorithms. 57-65, 1998. J. Bala, K.

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