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- Title
Enriching Song Recommendation Through Facial Expression Using Deep Learning.
- Authors
Deore, Shalaka Prasad
- Abstract
The music recommendation systems are highly linked with the emotional response of the user as the majority of the music is based on the mood of the listener. A large number of researches have been performed for the detection of emotion through the use of a variety of different techniques. These approaches have been helpful in achieving the emotion of the subject using various devices and other hardware which can be highly expensive with very low rates of accuracy. Whereas the detection of expression of the subject can be useful in determining the mood or the emotion with a considerable degree of accuracy. Therefore, to achieve the effective identification of emotion of an individual for effective music recommendation has been proposed in this research paper. The presented approach utilizes image normalization and Convolutional Neural Networks (CNN) which are trained on a dataset consisting of a number of different emotional responses. This trained model is then used to determine the mood of the individual and recommend music based on the detected mood. The experimental evaluation of the approach is performed to determine the accuracy of the emotion recognition which has resulted in highly accurate results. We achieved 62.88% testing accuracy with MSE and RMSE values of 8.5 and 2.9 respectively. The obtained results are promising and show that the fuzzy classification technique optimizes the outcomes.
- Subjects
FACIAL expression; DEEP learning; MOOD (Psychology)
- Publication
Ingénierie des Systèmes d'Information, 2023, Vol 28, Issue 1, p225
- ISSN
1633-1311
- Publication type
Academic Journal
- DOI
10.18280/isi.280126