• Latha H N
• Gayathri M S
• Kiran Bailey

Emotions are an important part of our life. A living being cannot live without emotions. The emotion of a person is affected by many things in and around it. A person's likes and dislikes vary according to his/her current emotion. Hence if we know the mood of the person we could recommend him/her the products that he/she would like to have during that situation. In this paper we have proposed a system which predicts the facial emotion of the person and then recommends a song play to the person. We have used the FER2013 [9] dataset for this purpose and have used different techniques for data generation, Image Super Resolution, and classification. A Fine Tuned Swin transformer model was used for classification. There were seven classes (emotions) in the dataset. The model could achieve an accuracy of 94.8% on the test-set with a best recall of 0.92 for disgust and happy classes and lowest recall of 0.29 for fear class.

Classification, recommendation, emotion, generative adversarial network, transformers

"Emotion Detection and Music Recommendation System using Super Resolution GAN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.g30-g35, June-2024, Available :http://www.jetir.org/papers/JETIR2406606.pdf

"Emotion Detection and Music Recommendation System using Super Resolution GAN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppg30-g35, June-2024, Available at : http://www.jetir.org/papers/JETIR2406606.pdf