• Prachi Singh
• Syed Rameez Ali
• Purnima Pandey
• Priya Shrivastava
• Rajesh Tiwari

Facial expression recognition (FER) is a critical task in human-computer interaction, emotion analysis, and affective computing. This paper presents a deep learning-based approach for recognizing facial expressions using Convolutional Neural Networks (CNNs), which have demonstrated exceptional performance in computer vision tasks. We propose a comprehensive framework that leverages a deep CNN architecture to automatically learn hierarchical features from facial images, enabling accurate recognition of a variety of facial expressions, including happiness, sadness, surprise, anger, fear, and disgust. Our approach eliminates the need for handcrafted features and complex pre-processing techniques by directly learning from raw pixel data. We evaluate the performance of the proposed model on publicly available FER datasets and compare it against traditional machine learning models and other state-of-the-art deep learning approaches. Experimental results show that our method achieves superior accuracy and robustness, outperforming existing systems in both controlled and real-world settings. The proposed system can be effectively used in applications ranging from emotion-aware systems to security and healthcare.

Facial Expression Recognition (FER) , Deep Convolutional Neural Networks (CNNs), Emotion Detection Affective Computing Human-Computer Interaction Feature Learning, Machine Learning, Computer Vision, Face Analysis Emotion Recognition Systems

""Facial Expression Recognition using Deep Convolutional Neural Networks: A Comprehensive Approach"", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.g601-g608, January-2025, Available :http://www.jetir.org/papers/JETIR2501664.pdf

""Facial Expression Recognition using Deep Convolutional Neural Networks: A Comprehensive Approach"", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. ppg601-g608, January-2025, Available at : http://www.jetir.org/papers/JETIR2501664.pdf