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Titlebook: Biometrics and ID Management; COST 2101 European W Claus Vielhauer,Jana Dittmann,Michael C. Fairhurst Conference proceedings 2011 Springer

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Dmitry Berdinsky,Prohrak Kruengthomyace is found when one final face class is retained. The standard UMIST and XM2VTS databases have been utilized to evaluate the performance of the proposed algorithm. Results show that it provides a good solution to the face classification problem.
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Features Extracted Using Frequency-Time Analysis Approach from Nyquist Filter Bank and Gaussian FiltST database with 130 speakers using Gaussian mixture speaker model. Results reveal that, the feature sets extracted using frequency-time analysis approach performs significantly better compared to the feature set extracted using time-frequency analysis approach.
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Entropy-Based Iterative Face Classificationce is found when one final face class is retained. The standard UMIST and XM2VTS databases have been utilized to evaluate the performance of the proposed algorithm. Results show that it provides a good solution to the face classification problem.
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Local Binary LDA for Face Recognitionn accuracy. The experimental results demonstrate the feasibility of the method for face recognition as follows: on XM2VTS face image database, a recognition accuracy of 96.44% is obtained using LBLDA, which is an improvement over LDA (94.41%). LBLDA can also outperform LDA in terms of computation speed.
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0302-9743 andsystems, handwriting authentication, speaker authentication, face recognition, multibiometric authentication, and on biometrics and forensics.978-3-642-19529-7978-3-642-19530-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
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https://doi.org/10.1007/978-3-642-19530-3biometric cryptosystems; biometric recognition; face classification; face verification; fingerprint anal
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