Title: Efficient deep mood-based Hindustani raga music recommendation using facial emotion expressions

Authors: Yogesh Prabhakar Pingle; Lakshmappa K. Ragha

Addresses: Department of Computer Engineering, Terna Engineering College, University of Mumbai, Mumbai, Maharashtra – 400706, India; Vidyavardhini's College of Engineering and Technology, University of Mumbai, India ' Department of Computer Engineering, Terna Engineering College, University of Mumbai, Mumbai, Maharashtra – 400706, India

Abstract: Music recommendation is considered as a solution, and the performance is degraded with prediction error. A novel approach for music recommendation based on facial emotions with the objective of extracting better feature information without loss is required. In this paper, an efficient cross-dense network model with multi-pooling is used to detect basic emotions from the face image. The complex cross-dense connections are provided for the extraction of most discriminate feature information. After recognising the emotion from the face, a new attention-based deep collaborative filtering recommendation system is proposed, with a list of Hindustani raga music to improve users' moods. The proposed framework is invoked with the Facial Expression Recognition 2013 - (FER-2013) dataset, and the recommendation is provided for happy and sad emotions from the ragas. The performance is compared with existing deep learning-based approaches. The proposed approach improves accuracy, precision and recall by 0.9972, 0.9896, and 0.9906.

Keywords: facial emotion recognition; CrossDenseNet; multi-pooling layer; AttentionNet; collaborative recommendation; Hindustani music.

DOI: 10.1504/IJRIS.2026.155206

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.4, pp.249 - 264

Received: 04 Dec 2024
Accepted: 06 Mar 2025
Published online: 29 Jul 2026 *

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