IMPROVING ACCURACY IN FACIAL EMOTION RECOGNITION THROUGH PCA AND ARTIFICIAL NEURAL NETWORKS

This research addresses the challenge of accurately identifying human emotions through facial expressions using Principal Component Analysis (PCA) combined with Artificial Neural Networks (ANN). The method involves preprocessing facial images, extracting critical features using PCA, and classifying emotional states with ANN. We utilized two standard facial expression datasets JAFFE and FEI for evaluation, focusing on basic emotions: happiness, sadness, surprise, and neutrality. Experimental results demonstrated that the proposed PCA-ANN approach achieved average accuracy rates of 96.3% on JAFFE and 93.8% on FEI datasets, outperforming several traditional methods in terms of computational efficiency and classification accuracy. Despite limitations concerning dataset size and emotion diversity, this research contributes to developing robust systems for real-world applications such as interactive technologies, assistive communication, and security systems. Future directions include expanding emotion recognition capabilities and integrating multimodal data for improved accuracy.