[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.9717/kmms.2021.24.4.528

Face Recognition using Correlation Filters and Support Vector Machine in Machine Learning Approach

Long, Hoang (Dept. of Artificial Intelligence Convergence, Pukyong National University)
Kwon, Oh-Heum (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Lee, Suk-Hwan (Dept. of Computer Engineering, Dong-A University)
Kwon, Ki-Ryong (Dept. of IT Convergence and Application Engineering, Pukyong National University)

Publication Information

Journal of Korea Multimedia Society / v.24, no.4, 2021 , pp. 528-537 More about this Journal

Abstract

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.

Keywords

correlation filters; MACE; MACH; UMACE; OT; SVM; machine learning; face recognition;

Citations & Related Records

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