Emotion recognition plays a crucial role in our day-to-day communication, and detecting emotions is one of the most formidable tasks in the field of human–computer Interaction (HCI). Facial expressions are the most straightforward and efficient way to identify emotions. With so many real-time applications, although automatic facial expression recognition (FER) is essential for numerous real-world applications in computer vision, developing a feature descriptor that accurately captures the subtle variations in facial expressions remains a significant challenge. Towards addressing this issue, a novel feature extraction technique inspired by Dining Philosophers Problem, named Dining Philosophers Problem Inspired Binary Patterns (DPIBP), has been proposed in this work. The proposed DPIBP methods extract three features in a local 5 × 5 neighborhood by considering the impact of both neighboring pixels and the adjacent pixels on the current pixel. To categorize facial expressions, the system used a multi-class Support Vector Machine (SVM) classifier. Reflecting real-world use, researchers tested the method on JAFFE, MUG, CK+, and TFEID benchmark datasets using a person-independent protocol. The proposed method, DPIBP, achieved superior performance compared to existing techniques that rely on manually crafted features for extraction.
Pallakonda et al. (Thu,) studied this question.