Open AccessExhaustive similarity search on a many-core architecture for finger-vein massive identification
Author(s) -
Sebastián Guidet,
Ricardo J. Barrientos,
Ruber Hernández-García,
Fernando Emmanuel Frati
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1702/1/012012
Subject(s) - computer science , identification (biology) , biometrics , block (permutation group theory) , hamming distance , data mining , graphics , graphics processing unit , process (computing) , similarity (geometry) , pattern recognition (psychology) , database , artificial intelligence , algorithm , image (mathematics) , mathematics , botany , geometry , computer graphics (images) , biology , operating system
In massive biometric identification systems, response times mainly depends on the database searching algorithms. Thus, in large databases, an increment in the simultaneous queries traffic becomes a critical factor. This paper proposes an algorithm based on the use of a graphic processing unit to solve the exhaustive similarity search for the mass identification of finger veins, using the binary pattern descriptor of the local vertical line and the Hamming distance. The proposed approach reduces the computation time of the searching process over high query traffic by solving each query with a different processing block. The proposed method allows the identification of individuals in a database of 1 million elements, which is the largest database used for finger-vein identification. Experimental results show that our proposed method resolves up to 28 queries simultaneously (over a database of one million individuals) within a time lower than 3 seconds and achieving a speed-up of 283x. To our knowledge, our work is the first implementation of finger-vein recognition on a general-purpose graphics processing unit, which is the main contribution of this document.