A NOVEL NEURAL NETWORK ARCHITECTURE FOR FACIAL EMOTION RECOGNITION

Abstract

Facial emotion recognition (FER) plays a vital role across diverse domains such as e-learning, marketing, humanoid robot interaction, HMI/HCI systems, and medical diagnostics. With the growing advancement of intelligent systems, there is a continuous effort to enhance the performance and accuracy of FER techniques. Traditional machine learning methods, including Random Forest (RF) and its variants, have been employed for emotion classification, but they often struggle with generalization, especially on diverse or complex facial datasets. To address these limitations, this study proposes a deep learning-based approach using the MobileNetV2 architecture, a lightweight yet efficient convolutional neural network widely adopted for mobile and real-time applications. The proposed MobileNetV2 model is fine-tuned for FER tasks to classify six basic emotions—sadness, anger, fear, surprise, disgust, and happiness—using facial images. Unlike conventional ML methods, MobileNetV2 automatically extracts and learns hierarchical features from facial data, eliminating the need for handcrafted features or manual partitioning strategies. This model demonstrates superior adaptability to varied image complexities while maintaining computational efficiency, making it well-suited for real-time emotion recognition on resource-constrained devices. By leveraging transfer learning and regularization techniques, the proposed MobileNetV2-based framework significantly improves emotion classification performance, providing a robust and accurate solution for real-world FER challenges.