By Jongsun Kim, Jongmoo Choi, Juneho Yi (auth.), Davide Maltoni, Anil K. Jain (eds.)
This booklet constitutes the refereed complaints of the overseas Biometric Authentication Workshop, BioAW 2004, held in Prague, Czech Republic, in might 2004, as a part of ECCV 2004.
The 30 revised complete papers offered have been conscientiously reviewed and chosen for presentation. The papers are prepared in topical sections on face acceptance, fingerprint acceptance, template safeguard and safety, different biometrics, and fusion and multimodal bioinformatics.
Read or Download Biometric Authentication: ECCV 2004 International Workshop, BioAW 2004, Prague, Czech Republic, May 15th, 2004. Proceedings PDF
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Extra resources for Biometric Authentication: ECCV 2004 International Workshop, BioAW 2004, Prague, Czech Republic, May 15th, 2004. Proceedings
The paper is structured as follow. In section 2 we first introduce the reader to the problem of face authentication. D. K. ): BioAW 2004, LNCS 3087, pp. 24–31, 2004. © Springer-Verlag Berlin Heidelberg 2004 Biometric Face Authentication Using Pixel-Based Weak Classifiers 25 Then, we present the proposed approach, boosting pixel-based classifiers for face authentication. We then compare our approach to state-of-the-results on the benchmark database XM2VTS. Finally, we analyze the results and conclude.
Discard those with eigenvalues sufficiently far from 0, keep c-1 eigenvectors of in most cases. Let Y be the first c-1 columns of V, which is the null space of we have 3. Diagonalize (usually a Perform eigen-analysis: matrix) which is full-rank. where is diagonal matrix sorted in decreasing order. The final projection matrix is: W is usually an matrix, which diagonalizes both the numerator and the denominator of Fisher’s criterion to matrices as (10), especially leads to a denominator of 0 matrix.
Experimental results demonstrate that the classification function learned using the proposed approach provides semantically more meaningful scoring than the reconstruction error used in AAM for classification between qualified and un-qualified face alignment. 1 Introduction Many image analysis and recognition application require alignment between an object in the input image and a target object. Alignment can have a great impact on the system performance. For examples, in appearance based face recognition, the alignment provide a more sensible foundation for template matching based recognition; the use of bad alignment can drop system performance significantly.
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