School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia.
Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam bin Abdulaziz University, Kharj 16278, Saudi Arabia.
Sensors (Basel). 2023 Mar 27;23(7):3513. doi: 10.3390/s23073513.
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.
已经有几项研究使用视觉和热面部图像来识别人类的情感状态。尽管热面部图像在识别自然人类情感方面具有优势，但很少有研究关注热图像中的面部遮挡挑战，特别是眼镜和面部毛发遮挡。因此，本文提出了三个分类模型来解决热图像中的面部遮挡问题，对六种基本的自发情绪进行分类。本文提出的第一个模型基于六个主要的面部区域，包括额头、鼻尖、脸颊、嘴和下巴。第二个模型将六个主要的面部区域分解成多个子区域，以研究子区域识别人类情感状态的效果。本文提出的第三个模型使用无眼镜和面部毛发（胡须、髭须）的选定面部子区域。在尖端和起始热图像上实现了九个统计特征。此外，还提出了四种特征选择技术和两种分类算法进行进一步研究。根据本文提出的对比分析，三种模态所得到的结果是有希望的，并且与其他研究的结果相当。
