Enriching Song Recommendation Through Facial Expression Using Deep Learning

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.

A Multi-Agent Systems Approach for Optimized Biomedical Literature Search

“…Authors of [15] investigates the intriguing intersection of emotion recognition via facial expressions and its application in music recommendation. While this paper primarily addresses music recommendation, the concept of emotion recognition through facial expressions can be extended to biomedical literature search.…”

Enhancing Online Teaching Effectiveness Through Computer Vision Analysis of Teacher Expressions and Gestures in Educational Videos

“…Although many studies have attempted to use computer vision technology to analyze teachers' facial expressions and movements, these methods often rely too much on static features and fail to fully consider the temporal changes and spatial distribution of expressions [14][15][16][17]. Additionally, some methods have low accuracy in recognizing complex expressions and movements, making it difficult to comprehensively evaluate teachers' teaching behaviors [18][19][20][21][22][23]. These limitations indicate the urgent need for more flexible and accurate analysis methods to improve the depth and breadth of research.…”