Affiliation:
1. School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
2. Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam bin Abdulaziz University, Kharj 16278, Saudi Arabia
Abstract
Several studies have been conducted using both visual and thermal facial images to identify human affective states. Despite the advantages of thermal facial images in recognizing spontaneous human affects, few studies have focused on facial occlusion challenges in thermal images, particularly eyeglasses and facial hair occlusion. As a result, three classification models are proposed in this paper to address the problem of thermal occlusion in facial images, with six basic spontaneous emotions being classified. The first proposed model in this paper is based on six main facial regions, including the forehead, tip of the nose, cheeks, mouth, and chin. The second model deconstructs the six main facial regions into multiple subregions to investigate the efficacy of subregions in recognizing the human affective state. The third proposed model in this paper uses selected facial subregions, free of eyeglasses and facial hair (beard, mustaches). Nine statistical features on apex and onset thermal images are implemented. Furthermore, four feature selection techniques with two classification algorithms are proposed for a further investigation. According to the comparative analysis presented in this paper, the results obtained from the three proposed modalities were promising and comparable to those of other studies.
Funder
Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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