Person Authentication using Relevance Vector Machine (RVM) for Face and Fingerprint
Автор: Long B. Tran, Thai H. Le
Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs
Статья в выпуске: 5 vol.7, 2015 года.
Бесплатный доступ
Multimodal biometric systems have proven more efficient in personal verification or identification than single biometric ones, so it is also a focus of this paper. Particularly, in the paper, the authors present a multimodal biometric system in which features from face and fingerprint images are extracted using Zernike Moment (ZM), the personal authentication is done using Relevance Vector Machine (RVM) and feature-level fusion technique. The proposed system has proven its remarkable ability to overcome the limitations of uni-modal biometric systems and to tolerate local variations in the face or fingerprint image of an individual. Also, the achieved experimental results have demonstrated that using RVM can assure a higher level of forge resistance and enables faster authentication than the state-of-the-art technique , namely the support vector machine (SVM).
Multimodal Biometric, Feature Level Fusion, Face, Fingerprint, Recognition System, Relevance vector machine, Zernike moment
Короткий адрес: https://sciup.org/15014754
IDR: 15014754
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