Biometric Authentication: ECCV 2004 International Workshop, by Jongsun Kim, Jongmoo Choi, Juneho Yi (auth.), Davide

April 3, 2017 | Computers | By admin | 0 Comments

By Jongsun Kim, Jongmoo Choi, Juneho Yi (auth.), Davide Maltoni, Anil K. Jain (eds.)

This booklet constitutes the refereed court cases of the overseas Biometric Authentication Workshop, BioAW 2004, held in Prague, Czech Republic, in could 2004, as a part of ECCV 2004.

The 30 revised complete papers offered have been conscientiously reviewed and chosen for presentation. The papers are equipped in topical sections on face attractiveness, fingerprint reputation, template safeguard and defense, different biometrics, and fusion and multimodal bioinformatics.

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Additional resources for Biometric Authentication: ECCV 2004 International Workshop, BioAW 2004, Prague, Czech Republic, May 15th, 2004. Proceedings

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8] R. Cappelli, D. Maio, and D. Maltoni. Subspace classification for face recognition. Biometric Authentication, pages 133–142, 2002. 22 L. Zhang and D. I. W. J. Flynn. Multi-modal 2d and 3d biometrics for face recognition. AMFG, pages 187–194, 2003. [10] R. L. Wilson, and S. Sirohey. Human and machine recognition of faces: A survey. PIEEE, 83(5):705–740, May 1995. S. N. J. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. PAMI, 23(6):643–660, June 2001.

In UMD Technical Report CAR-TR948, 2000. Y. Zhao and R. Chellappa. Illumination-insensitive face recognition using symmetric shape-from-shading. In CVPR, pages I: 286–293, 2000. ch Abstract. The performance of face authentication systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machines or Multi-layer Perceptrons. Unfortunately, these classifiers involve thousands of parameters that are difficult to store on a smart-card for instance.

The FERET database (6×70). All the images are resized to 92×112. There are facial expression, illumination and pose variations. Fig. 2. Samples from the mixture database The mixture database is divided into two non-overlapping set for training and testing. The training dataset consists of 500 images: 5 images, 6 images and 3 images per person are randomly selected from the ORL, the YALE database and the FERET subset respectively. The remaining 485 images are used for testing. In order to reduce the influence of some extreme illumination, histogram equalization is applied to the images as pre-processing.

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