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ADESHINA SIRAJDIN OLAGOKE(Student Member, IEEE) was born in March 1981. He received the B.Eng. degree in electrical and electronic engineering from the Federal University of Technology Yola (FUTY), Adamawa, in 2005, and the master's degree in signal processing from the University of Maiduguri, in 2015. He is currently pursuing the Ph.D. degree in image processing and with the School of Electrical and Electronic Engineering, Universiti Sains Malaysia. He is also a Graduate Research Assistant with the School of Electrical and Electronic Engineering, Universiti Sains Malaysia. He is also a native of Offa, in Kwara, Nigeria. His current research interest is in the field of face recognition and detection. He has experience in computer networks and system maintenance. From 2007 to 2010, he worked as a Network Engineer and a System Administrator in Yaysib Wireless Networks and Computers. He is also a certified Cisco Associate and CompTIA. He is a Lecturer with the Department of Computer Engineering, Federal Polytechnic Mubi, Adamawa, Nigeria. He already published some articles. He is a member of the Nigerian Society of Engineering, and a registered Engineer with the Council for Regulation of Engineering and Engineering Practice in Nigeria.
Face detection by electronic systems has been leveraged by private and government establishments to enhance the effectiveness of a wide range of applications in our day to day activities, security, and businesses. Most face detection algorithms that can reduce the problems posed by constrained and unconstrained environmental conditions such as unbalanced illumination, weather condition, distance from the camera, and background variations, are highly computationally intensive. Therefore, they are primarily unemployable in real-time applications. This paper developed face detectors by utilizing selected Haar-like and local binary pattern features, based on their number of uses at each stage of training using MATLAB’s trainCascadeObjectDetector function. We used 2577 positive face samples and 37,206 negative samples to train Haar-like and LBP face detectors for a range of False Alarm Rate (FAR) values (i.e., 0.01, 0.05, and 0.1). However, the study shows that the Haar cascade face detector at a low stage (i.e., at six stages) for 0.1 FAR value is the most efficient when tested on a set of classroom images dataset with 100% True Positive Rate (TPR) face detection accuracy. Though, deep learning ResNet101 and ResNet50 outperformed the average performance of Haar cascade by 9.09% and 0.76% based on TPR, respectively. The simplicity and relatively low computational time used by our approach (i.e., 1.09 s) gives it an edge over deep learning (139.5 s), in online classroom applications. The TPR of the proposed algorithm is 92.71% when tested on images in the synthetic Labeled Faces in the Wild (LFW) dataset and 98.55% for images in MUCT face dataset “a”, resulting in a little improvement in average TPR over the conventional face identification system.
Sirajdin Adeshina; Haidi Ibrahim; Soo Teoh; Seng Hoo. Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons. Electronics 2021, 10, 102 .
AMA StyleSirajdin Adeshina, Haidi Ibrahim, Soo Teoh, Seng Hoo. Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons. Electronics. 2021; 10 (2):102.
Chicago/Turabian StyleSirajdin Adeshina; Haidi Ibrahim; Soo Teoh; Seng Hoo. 2021. "Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons." Electronics 10, no. 2: 102.