EMOTION-AWARE COLOR THEORY: COMPUTATIONAL MODELING OF ARTISTIC COLOR HARMONIES IN DESIGN - Pandey, Suman; Porwal, Tina; Sule, Bipin; Yadav, Raman; Sadhale, Manasi; Randhave, Mahesh Rangnath (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Color is a determinant aspect of visual communication, which defines aesthetic harmony and causes emotions in design practices. Nevertheless, traditional color theory is based on the fixed principles and subjective perception to a significant extent, which does not allow it to be applicable to various emotional backgrounds. This paper suggests an Emotion-Aware Color Theory that is a computationally based model of artistic color harmonies that combines affective computing with the data-driven analysis of colors. The framework is a combination of emotion representations models, such as valence/arousal dimensions and discrete emotions, and perceptually based color items, such as hue, saturation, brightness and contrast. The models of machine learning and deep learning are used to learn nonlinear mappings between the emotive conditions and harmonious color structures, with a multi-objective optimization being used to balance between the aesthetic harmony and emotional consistency. Experimental analysis shows that the suggested method is better than the conventional rule-based and purely aesthetic models of predicting emotionally fit color pallets in different design cases. The results of the analysis are greater consistency, flexibility, and interpretability of color recommendations based on emotions. Regardless of such developments, there are still issues in terms of dataset bias, intercultural differences between color and emotion correlations, and real-time individualization. The research paper concludes that emotion-conscious computational color modeling can be used as a strong base of next-generation design systems, which provide adaptive, emotional visual experiences.