Context aware facial expression recognition with deep learning architectures

Facial expression recognition (FER) plays a pivotal role in applications such as mental health diagnosis, security, marketing, human-robot interaction, healthcare, education, and gaming. However, challenges like varied facial poses, uneven lighting, and the presence of facial accessories often hinder accurate detection. Traditional methods frequently struggle with effectiv e feature extraction and classification. To address these limitations, this study proposes a robust facial expression recognition architecture based on Convolutional Neural Networks (CNNs) coupled with advanced preprocessing techniques. The model effectively mitigates issues such as lighting variations and class imbalances while achieving enhanced recognition accuracy. A comprehensive evaluation using k-fold cross-validation was conducted on the CK+ dataset, renowned for its high-quality labeled images of primary emotions. The proposed model achieved an accuracy of 96%, significantly outperforming established benchmarks, including VGG-19 (90%), ResNet50 (92%), and MobileNet (94%). These results underscore the efficacy of the CNN-based approach in advancing FER accuracy. Future work will focus on extending this research to real-time facial expression detection, leveraging transfer learning to adapt the model to diverse datasets, and integrating emotio n recognition with multimodal data such as speech and EEG signals to broaden its applicability across industries.