• M.AnushaRani
• B. PrasannaLakshmi
• D.Hemalatha
• N.AkhilaRani
• A.Likitha

Facial emotion recognition (FER) has become increasingly significant in fields such as human-computer interaction, mental health assessment, and social robotics. This paper explores the application of deep learning techniques, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of emotion detection from facial expressions. We examine various datasets, including FER-2013 and CK+, which provide the necessary labelled images for effective model training and evaluation. The discussion also addresses key challenges, such as data imbalance, cultural differences in emotional expression, and the impact of environmental factors like lighting and occlusion on performance. The development of more robust and adaptable emotion recognition systems, paving the way for their integration into real-world applications across multiple domains.

ADVANCED DEEP LEARNING TECHNIQUES FOR FACIAL EMOTION RECOGNITION

"ADVANCED DEEP LEARNING TECHNIQUES FOR FACIAL EMOTION RECOGNITION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.440-448, November-2024, Available :http://www.jetir.org/papers/JETIRGO06046.pdf

"ADVANCED DEEP LEARNING TECHNIQUES FOR FACIAL EMOTION RECOGNITION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp440-448, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06046.pdf