Music Recommender System for Users Based on Emotion Detection through Facial Features

Ahlam, Alrihaili; Alsaedi, Alaa S.; Albalawi, Kholood; Syed, Liyakathunisa

doi:10.1109/dese.2019.00188

Cited by 18 publications

(5 citation statements)

References 8 publications

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“…The Machine Learning Algorithms used are Viola-Jones for HAAR features and feature extraction, PCA, CNN, SVM for feature vectors and multi class Adaboost during dynamic time warping. [1]. The study highlights the implementation of machine learning techniques to develop a sophisticated system capable of retrieving and recommending music, catering to individual preferences and enhancing the user's music discovery experience.…”

Section: Literature Reviewmentioning

confidence: 99%

FeelFlow: Music Recommendation System Based on your Facial Expression

Navale,

Phand,

Patil

et al. 2023

IJRASET

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This study introduces an innovative Emotion-Aware Music Recommender System that leverages facial feature analysis for real-time emotion detection, aiming to elevate the personalization and user engagement in music recommendations. Through the integration of computer vision and machine learning techniques, the system interprets facial expressions to discern user’s emotional states before recommending music to them. Integrating advanced deep learning and evolutionary techniques improves the accuracy and efficiency of facial expression detection. Implementation of the proposed recommender system is performed using Viola-Jones algorithm, CNN, Principal Component Analysis (PCA) techniques. Experimental results confirm the effectiveness of this method and demonstrate its potential to improve music recommendations

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Section: Literature Reviewmentioning

confidence: 99%

FeelFlow: Music Recommendation System Based on your Facial Expression

Navale,

Phand,

Patil

et al. 2023

IJRASET

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“…The fundamental target of face identification method is to recognize the face in the edge by lessening the outer clamours and different elements [9]. The means engaged with the FACE Identification Cycle are Facial feelings are considered as the most pivotal figure individuals' correspondence which empower us to see others' aims [10].…”

Section: Face Capturing Using Webcammentioning

confidence: 99%

A heuristic technique for music recommendation using Haar cascade classifier through facial expressions

Karrothu¹,

Lavanya²,

Harish³

et al. 2023

ACE

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Human emotion assumes a fundamental part lately. Feeling depends on human sentiments which can be both communicated or not. Feelings communicate the human's singular way of behaving which can be in various structures. Extraction of the emotion states people individual condition of conduct. The goal of this task is to separate element from human face and identify feeling and to play music as indicated by the emotion identified. In this task, fostering a model to suggest dynamic music proposal framework in view of human feelings is our principal perspective. From the genuine face the emotion is recognized and when the mind-set is gotten from input picture, particular melodies for the state of mind would be played to hold the clients. In this methodology, the application gets associated with human sentiments accordingly giving an individualized touch to the clients. Our projected framework focuses on distinguishing the human face using Haar Cascade classifier. A few stages like face location utilizing webcam, facial feature extraction, look identification, with that interfacing with a music player will be followed. For exploratory outcomes, Haar cascade calculation and Computer vision along with CNN (Convolution Neural Network) for feeling discovery and music suggestion.

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“…The proposed system detects the emotions of a person, if the person has a negative emotion, then a certain playlist will be shown that includes the most related types of music that will enhance his mood. And if the emotion is positive, a specific playlist will be presented which contains different types of music that will inflate the positive emotions [4].…”

Section: Journal Of Informatics Electrical and Electronicsmentioning

confidence: 99%

Music Recommendation Based on Face Emotion Recognition

Athavle¹,

Mudale²,

Shrivastav³

et al. 2021

J. Infor. Electr. Electron. Eng.

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We propose a new approach for playing music automatically using facial emotion. Most of the existing approaches involve playing music manually, using wearable computing devices, or classifying based on audio features. Instead, we propose to change the manual sorting and playing. We have used a Convolutional Neural Network for emotion detection. For music recommendations, Pygame & Tkinter are used. Our proposed system tends to reduce the computational time involved in obtaining the results and the overall cost of the designed system, thereby increasing the system’s overall accuracy. Testing of the system is done on the FER2013 dataset. Facial expressions are captured using an inbuilt camera. Feature extraction is performed on input face images to detect emotions such as happy, angry, sad, surprise, and neutral. Automatically music playlist is generated by identifying the current emotion of the user. It yields better performance in terms of computational time, as compared to the algorithm in the existing literature.

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Music Recommender System for Users Based on Emotion Detection through Facial Features

Cited by 18 publications

References 8 publications

FeelFlow: Music Recommendation System Based on your Facial Expression

FeelFlow: Music Recommendation System Based on your Facial Expression

A heuristic technique for music recommendation using Haar cascade classifier through facial expressions

Music Recommendation Based on Face Emotion Recognition

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