A Novel Approach For Finger Vein Verification Based on Self-Taught Learning
عنوان مقاله: A Novel Approach For Finger Vein Verification Based on Self-Taught Learning
شناسه ملی مقاله: ICMVIP09_039
منتشر شده در نهمین کنفرانس ماشین بینایی و پردازش تصویر ایران در سال 1394
شناسه ملی مقاله: ICMVIP09_039
منتشر شده در نهمین کنفرانس ماشین بینایی و پردازش تصویر ایران در سال 1394
مشخصات نویسندگان مقاله:
Mohsen Fayyaz - Malek-Ashtar University of Technology Tehran, Iran
Mohammad HajizadehـSaffar
Mohammad Sabokrou
Mojtaba Hoseini1 - Malek-Ashtar University of Technology
خلاصه مقاله:
Mohsen Fayyaz - Malek-Ashtar University of Technology Tehran, Iran
Mohammad HajizadehـSaffar
Mohammad Sabokrou
Mojtaba Hoseini1 - Malek-Ashtar University of Technology
In this paper, we propose a method for user Finger Vein Authentication (FVA) as a biometric system. Using the discriminative features for classifying theses finger veins is one of the main tips that make difference in related works, thus we propose to learn a set of representative features, based on auto-encoders. We model the represented users’ finger vein structure using a Gaussian distribution. Experimental results show that our method performs like a state-of-the-art method on SDUMLA-HMT benchmark
کلمات کلیدی: Self-Taught Learning, Feature Learning, Finger Vein Verification, Biometric Verification
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/568566/