• Gudiwaka Vijayalakshmi
• Pampana Nandini

CNNs have achieved significant progress in several domains, notably in computer vision. Their application in analyzing images and distinguishing facial expressions [1] with exceptional accuracy has been particularly noteworthy. A framework based on convolutional neural networks [2] has been developed to identify human emotions [3] in real-time, leveraging dynamic facial expressions. The framework encompasses three stages: initial face detection [4] utilizing Haar Cascades [5], subsequent normalization, and emotion recognition [6] through a CNN [7] trained on the FER 2013 dataset [8], comprising seven distinct emotions. Experimental outcomes unequivocally demonstrate the feasibility [9] of facial emotion recognition, offering educators the opportunity to customize their presentations [10] based on their students' emotional states.

facial expressions, human emotions, haar cascades, feasibility, recognition, emotional states

"DETECTING FACIAL EXPRESSION OF EMOTIONS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.i148-i154, June-2023, Available :http://www.jetir.org/papers/JETIR2306818.pdf

"DETECTING FACIAL EXPRESSION OF EMOTIONS USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppi148-i154, June-2023, Available at : http://www.jetir.org/papers/JETIR2306818.pdf