Biometric identification based on feature fusion with PCA and SVM

László Lefkovits; Szidónia Lefkovits; Simina Emerich

doi:10.1117/12.2309533

13 April 2018 Biometric identification based on feature fusion with PCA and SVM

László Lefkovits, Szidónia Lefkovits, Simina Emerich

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 1069604 (2018) https://doi.org/10.1117/12.2309533
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

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.

Citation Download Citation

László Lefkovits, Szidónia Lefkovits, and Simina Emerich "Biometric identification based on feature fusion with PCA and SVM", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 1069604 (13 April 2018); https://doi.org/10.1117/12.2309533

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