<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Traditional methods for face recognition do not scale well with the number of training sample, which limits the wide applications of related techniques. We propose an improved Support Vector Clustering algorithm to handle the large-scale biometric feature data effectively. We prove theoretically that the proposed algorithm converges to the optimum within any given precision quickly. Compared to related state-of-the-art Support Vector Clustering algorithms, it has the competitive performances on both training time and accuracy. Besides, we use the proposed algorithm to handle classification problem, and face recognition, as well. Experiments on synthetic and real-world data sets demonstrate the validity of the proposed algorithm.</p>
</abstract>

Traditional methods for face recognition do not scale well with the number of training sample, which limits the wide applications of related techniques. We propose an improved Support Vector Clustering algorithm to handle the large-scale biometric feature data effectively. We prove theoretically that the proposed algorithm converges to the optimum within any given precision quickly. Compared to related state-of-the-art Support Vector Clustering algorithms, it has the competitive performances on both training time and accuracy. Besides, we use the proposed algorithm to handle classification problem, and face recognition, as well. Experiments on synthetic and real-world data sets demonstrate the validity of the proposed algorithm.

Face Recognition Based On Improved Support Vector Clustering

Zhengzhou Institute of Aeronautical Industry Management

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Face Recognition Based On Improved Support Vector Clustering"}

Traditional methods for face recognition do not scale well with the number of training sample, which limits the wide applications of related techniques. We...

Face Recognition Based On Improved Support Vector Clustering

Data publikacji: 01 gru 2014

Zakres stron: 1807 - 1829

Otrzymano: 06 lip 2014

Przyjęty: 05 lis 2014

DOI: https://doi.org/10.21307/ijssis-2017-734

Słowa kluczowe
Face recognition, biometric feature, Support Vector Clustering, kernel methods, classification, large-scale data

© 2014 Yongqing Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Face Recognition Based On Improved Support Vector Clustering

Data publikacji: 01 gru 2014

Zakres stron: 1807 - 1829

Otrzymano: 06 lip 2014

Przyjęty: 05 lis 2014

DOI: https://doi.org/10.21307/ijssis-2017-734

Słowa kluczoweFace recognition, biometric feature, Support Vector Clustering, kernel methods, classification, large-scale data

© 2014 Yongqing Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Face recognition, biometric feature, Support Vector Clustering, kernel methods, classification, large-scale data