• Srinivas Brahmoji

It's an intriguing and difficult problem to study how people react emotionally to visual stimuli like pictures and movies, which is called visual sentiment analysis. It makes an effort to comprehend visual data's high-level content. The success of modern models can be linked to the creation of reliable computer vision algorithms. Most of the current models suggest either robust features or more sophisticated models as ways to address the issue. The main suggested inputs are primarily visual elements from the entire image or video. Local areas, which we believe are quite relevant to people's emotional reaction to the entire image, have not received much attention. Image recognition software is used to identify persons in pictures and assess their feelings. The CNN algorithm is used in this project to carry out that task. When given an image, the programme would look for faces, recognise them, place rectangles in the appropriate places, characterise the emotions it discovered, and display the percentage of emotions.

Face emotion recognition, computer vision, convolutional neural networks, deep learning, hass arcade, fer-2013.

"Facial Emotion Recognition Using CNN And Computer Vision", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 4, page no.1460-1468, April-2018, Available :http://www.jetir.org/papers/JETIR1804459.pdf

"Facial Emotion Recognition Using CNN And Computer Vision", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 4, page no. pp1460-1468, April-2018, Available at : http://www.jetir.org/papers/JETIR1804459.pdf