Facial Geometric Feature Extraction for Dimensional Emotion Analysis Using Genetic Programming

Abstract

Geometric features derived from single static images have the potential to be highly effective for facial emotion analysis, as shape, structure, and spatial relationships are key factors. However, these aspects are rarely explored in existing research. In this paper, we propose a novel approach that utilizes Genetic Programming (GP) to automatically extract geometric features for more effective emotional representation. The proposed GP system uses various evaluation strategies, evolving either a single feature per run or multiple features within a single run. These GP-evolved features capture critical angular and distance-based relationships between facial landmarks, which are then integrated with an existing deep learning model to enhance performance. The results show that the proposed method achieves improved performance in dimensional emotion analysis, providing a more comprehensive understanding of emotional expressions in static images. In addition, our approach is effective in improving the accuracy of emotion predictions, establishing a foundation for more precise facial emotion analysis using geometric information.

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Reproductions