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Biometric identification based on feature fusion with PCA and SVM
Author(s): László Lefkovits; Szidónia Lefkovits; Simina Emerich
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Paper Abstract

Biometric identification is gaining ground compared to traditional identification methods. Many biometric measurements may be used for secure human identification. The most reliable among them is the iris pattern because of its uniqueness, stability, unforgeability and inalterability over time. The approach presented in this paper is a fusion of different feature descriptor methods such as HOG, LIOP, LBP, used for extracting iris texture information. The classifiers obtained through the SVM and PCA methods demonstrate the effectiveness of our system applied to one and both irises. The performances measured are highly accurate and foreshadow a fusion system with a rate of identification approaching 100% on the UPOL database.

Paper Details

Date Published: 13 April 2018
PDF: 9 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 1069604 (13 April 2018); doi: 10.1117/12.2309533
Show Author Affiliations
László Lefkovits, Sapienta Univ. of Tîrgu-Mureș (Romania)
Technical Univ. of Cluj-Napoca (Romania)
Szidónia Lefkovits, "Petru Maior" Univ. of Tîrgu-Mureș (Romania)
Simina Emerich, Technical Univ. of Cluj-Napoca (Romania)


Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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