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Abstract
Facial expressions serve as crucial indicators of an individual's psychological state, playing a pivotal role in face-to-face communication. This research focuses on advancing collaboration between machines and humans by undertaking a thorough investigation into facial expressions. Specifically, we delve into the analysis of emotional variations related to changes in skin tone across different genders and cultural backgrounds (Black and white). The research methodology is structured across three phases. In Phase I, image data is acquired and meticulously processed from the Chicago face dataset, resulting in 12,402 augmented images across five classes (Normal case, Benign case, Adenocarcinoma, Squamous-cell-carcinoma, Large-cell-carcinoma). Phase II involves the identification of Regions of Interest (ROI) and the extraction of RGB values as features from these ROIs. Various methods, including those proposed by Kovac, Swift, and Saleh, are employed for precise skin identification. The final phase, Phase III, centers on the in-depth analysis of emotions and presents the research findings. Statistical techniques, such as Descriptive statistics, independent sample T-tests for gender and cross-cultural comparisons, and two-way ANOVA, are applied to RED, BLUE, and GREEN pixel values as response variables, with gender and emotions as explanatory variables. The rejection of null hypotheses prompts a Post Hoc test to discern significant pairs of means. The results indicate that both cross-cultural backgrounds and gender significantly influence pixel colors, underscoring the impact of different localities on pixel coloration. Across various expressions, our results exhibit a minimal 0.05% error rate in all classifications. Notably, the study reveals that green pixel color does not exhibit a significant difference between Anger and Neutral emotions, suggesting a near-identical appearance for green pixels in these emotional states. These findings contribute to a nuanced understanding of the intricate relationship between facial expressions, gender, and cultural backgrounds, providing valuable insights for future research in human–machine interaction and emotion recognition.
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1 University of Education, Department of Information Sciences, Lahore, Pakistan (GRID:grid.440554.4) (ISNI:0000 0004 0609 0414)
2 Institute of Southern Punjab, Multan, Pakistan (GRID:grid.440554.4)
3 Metaverse Research Institute, Guangzhou University, School of Computer Science and Cyber Engineering, Guangzhou, People’s Republic of China (GRID:grid.411863.9) (ISNI:0000 0001 0067 3588)
4 University of Education, Department of Information Sciences, Lahore, Pakistan (GRID:grid.440554.4) (ISNI:0000 0004 0609 0414); Shenzhen University, IOT Laboratory, Shenzhen, China (GRID:grid.263488.3) (ISNI:0000 0001 0472 9649)
5 Shenzhen University, IOT Laboratory, Shenzhen, China (GRID:grid.263488.3) (ISNI:0000 0001 0472 9649)
6 Huanggang Normal University, College of Computer Science, Huanggang, China (GRID:grid.443405.2) (ISNI:0000 0001 1893 9268)
7 Al Ain University, Department of Computer Science, Al Ain, UAE (GRID:grid.444473.4) (ISNI:0000 0004 1762 9411)
8 Princess Nourah Bint Abdulrahman University, Department of Information Systems, College of Computer and Information Sciences, Riyadh, Saudi Arabia (GRID:grid.449346.8) (ISNI:0000 0004 0501 7602)
9 South Valley University, Computer Science Department, Faculty of Computers and Information, Qena, Egypt (GRID:grid.412707.7) (ISNI:0000 0004 0621 7833); New Assiut Technological University (N.A.T.U.), Faculty of Industry and Energy Technology, New Assiut City, Egypt (GRID:grid.412707.7)