Facial Emotion Recognition using Deep Learning: Advancements, Challenges, and Future Directions

Abstract

Abstract The study of facial emotion recognition (FER) holds significant importance within the realm of academic research, since it has wide-ranging ramifications across multiple areas such as mental health assessment and human-computer interaction. This work introduces a novel methodology for FER that integrates Gabor filter-based feature extraction with a deep Convolutional Neural Network (CNN). The utilisation of Gabor filters enables extraction of prominent textural characteristics from facial images, whilst CNNs acquire informative representations to achieve precise emotion classification. The proposed methodology is assessed using the FER2013 dataset and compared with pre-existing methodologies. The findings illustrate the efficacy of our methodology in accurately identifying facial expressions of emotions, emphasising its potential for practical implementation in the fields of mental health research and emotion-sensitive systems. The method demonstrates improved accuracy and resilience by combining Gabor filters and CNNs, showing potential for enhancing mental health evaluation and enabling adaptive human-computer interaction. This study makes a valuable contribution to the fields of health, mental health, and adaptation by advancing the creation of emotion-aware technologies that are designed to address the unique emotional requirements of individuals.