Optimizing Facial Muscle Activation Features for Emotion Recognition: A Metaheuristic Approach Using Inner Triangle Points

Facial Expression Recognition (FER) is a critical component of affective computing, with deep learning models dominating performance metrics. In contrast, geometric approaches based on the Facial Action Coding System (FACS) offer explainability through using triangles aligned to facial landmarks. The notable points of these triangles capture the deformation of muscles. However, restricting the feature extraction to notable points may be suboptimal. This paper introduces a novel method for optimizing the extraction of features by searching for optimal inner points in 22 facial triangles applying three metaheuristics: Differential Evolution (DE), Particle Swarm Optimization (PSO), and Convex Partition (CP). This results in a set of 59 geometric-based descriptors that capture muscle deformation more accurately. The proposed method was evaluated using five machine learning classifiers on two benchmark databases: the Karolinska Directed Emotional Faces (KDEF) and the Japanese Female Facial Expression (JAFFE). Experimental results demonstrate significant performance improvements. The combination of DE with a Multi-Layer Perceptron (MLP) achieved an accuracy of 0.91 on the KDEF database, while Support Vector Machine (SVM) optimized via CP attained an accuracy of 0.81 on the JAFFE database. Statistical analysis confirms that optimized descriptors yield higher accuracy than previous geometric methods.