Face Recognition by Principal Component Regression using Hypercomplex Numbers

Kamal, Aliaa Tarik; El-Melegy, Moumen Taha; El-Hawary, Hassan Mohamed; Hussein, Khaled Fathy

doi:10.21608/aunj.2022.131391.1006

	Face Recognition by Principal Component Regression using Hypercomplex Numbers
Assiut University Journal of Multidisciplinary Scientific Research
Volume 51, Issue 3, September 2022, Page 268-278 PDF (915.88 K)
Document Type: Novel Research Articles
DOI: 10.21608/aunj.2022.131391.1006
View on SCiNiTO
Authors
Aliaa Tarik Kamal ¹; Moumen Taha El-Melegy²; Hassan Mohamed El-Hawary³; Khaled Fathy Hussein⁴
¹Computer Science Department, Faculty of Computers and Information, Assiut University, Assiut, Egypt
²Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut, Egypt
³Mathematics Department, Faculty of Science, Assiut University, Assiut, Egypt
⁴Computer Science Department, Faculty of Computers and Information, Assiut University, Assiut, Assiut, Egypt
Abstract
Linear regression is one of the simplest and widely used machine learning algorithms that has received a lot of attention in many fields. Linear Regression Classification (LRC) algorithm depends on represents each class's training images independently in a linear regression relationship. The algorithm depends on applying the least squares method to find the regression coefficient then decides the class label with the smallest reconstruction error. In this paper, we propose a classification by principal component regression (CbPCR) strategy, which depends on performing regression of each data class in terms of its principal components. This CbPCR formulation leads to a novel formulation of the LRC problem that keeps the key information of the data classes while providing more compact closed-form solutions. We also extend this strategy to the 4D hypercomplex domains to take into account the color information of the image. Our experiments on two color face recognition benchmark databases prove the efficacy of the proposed strategy.
Keywords
Face recognition; Linear regression classification; Principal component analysis; Hypercomplex numbers


Statistics Article View: 150 PDF Download: 139