Gabor and Maximum Response Filters with Random Forest Classifier for Face Recognition in the Wild
Yuen-Chark See1, Eugene Liew1, and Norliza Mohd Noor2
1Department of Electrical and Electronic Engineering, University Tunku Abdul Rahman, Malaysia
2Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Malaysia
Abstract: Research on face recognition has been evolving for decades. There are numerous approaches developed with highly desirable outcomes in constrained environments. In contrast, approaches to face recognition in an unconstrained environment where varied facial posing, occlusion, aging, and image quality still pose vast challenges. Thus, face recognition in the unconstrained environment still an unresolved problem. Many current techniques are not performed well when experimented in unconstrained databases. Additionally, most of the real-world application needs a good face recognition performance in the unconstrained environment. This paper presents a comprehensive process aimed to enhance the performance of face recognition in an unconstrained environment. This paper presents a face recognition system in an unconstrained environment. The fusion between Gabor filters and Maximum Response (MR) filters with Random Forest classifier is implemented in the proposed system. Gabor filters are a hybrid of Gabor magnitude filters and Oriented Gabor Phase Congruency (OGPC) filters. Gabor magnitude filters produce the magnitude response while the OGPC filters produce the phase response of Gabor filters. The MR filters contain the edge- and bar-anisotropic filter responses and isotropic filter responses. In the face features selection process, Monte Carlo Uninformative Variable Elimination Partial Least Squares Regression (MC-UVE-PLSR) is used to select the optimal face features in order to minimize the computational costs without compromising the accuracy of face recognition. Random Forests is used in the classification of the generated feature vectors. The algorithm performance is evaluated using two unconstrained facial image databases: Labelled Faces in the Wild (LFW) and Unconstrained Facial Images (UFI). The proposed technique used produces encouraging results in these evaluated databases in which it recorded face recognition rates that are comparable with other state-of-the-art algorithms.
Keywords: Face recognition, labelled faces in the wild, unconstrained facial images.
Received November 7, 2019; accept February 7, 2021