• CH.NANDA KUMAR
• E.NITHIN
• Ch. Bindu madhavi
• Ch. Sai Krishna

Facial emotion recognition is essential across various fields, yet automating this process remains challenging for computer algorithms. This paper introduces an innovative approach combining deep Convolutional Neural Networks (CNNs) with image edge detection to enhance accuracy. By integrating dropout regularization to mitigate overfitting, our method achieves an impressive 92.81% average accuracy rate across eight basic emotion classes using the extended Cohn Kanade (CK+) dataset. Additionally, we provide a concise overview of current facial emotion recognition techniques and datasets, aiming to deepen understanding and facilitate comparisons in this vital field of computer vision and machine learning.

Convolutional neural networks, Facial emotion recognization, deep learning methodologies, computer vision techniques, emotion recognition systems.

"ADVANCING FACIAL EMOTION RECOGNITION:INSIGHTS AND INNOVATIONS IN DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.a357-a366, March-2024, Available :http://www.jetir.org/papers/JETIR2403045.pdf

"ADVANCING FACIAL EMOTION RECOGNITION:INSIGHTS AND INNOVATIONS IN DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppa357-a366, March-2024, Available at : http://www.jetir.org/papers/JETIR2403045.pdf