Journal of Soft Computing Paradigm
IRO Journals
Volume 5 — Issue 2 — June 2023

Design of a Music Recommendation Device Using Mini-Xception CNN and Facial Recognition

https://doi.org/10.36548/jscp.2023.2.007
Chandan Singh
Department of Networking and Communication SRM Institute of Science and Technology Chennai, India
V Himayanth
Department of Networking and Communication SRM Institute of Science and Technology Chennai, India
B. Balakiruthiga
Department of Networking and Communication SRM Institute of Science and Technology Chennai, India
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How to Cite

Singh, Chandan, V Himayanth, and B. Balakiruthiga. 2023. “Design of a Music Recommendation Device Using Mini-Xception CNN and Facial Recognition”. Journal of Soft Computing Paradigm 5 (2): 181-93. https://doi.org/10.36548/jscp.2023.2.007.
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Keywords

— Artificial Intelligence (AI)
— Machine Learning (ML)
— Deep Convolutional Neural Network (DCNN)
— Facial recognition-based Music Recommendation System (MRS)
Published: 30-06-2023

Abstract

Due to the emerging developments in Artificial Intelligence and Machine Learning Technologies, various prediction systems are been developed based on human emotions and real time aspects of human psychology as well. Facial recognition system is one such mechanism which is the most vibrant strategy used for predicting human emotions. It is extensively applied in surveillance systems, fault identification and other security related aspects. Based on the human emotions researchers have already proposed several music recommendation systems. This paper aims to propose a Facial recognition-based music recommendation system to treat the psychology patients. This helps to recover the patients from mental stress, anxiety, and depression. The suggested method aims to take into account the limitations of the face recognition system in current frameworks, such as the requirement to lower the processing delay for deep feature extraction and the necessity to design a Mini exception technique based on Deep Convolutional Neural Network (DCNN) architecture. The FER- 2013 image dataset, which consists of 35000 face photos with automated labelling is considered. It is used to determine how well the proposed approach would detect the various emotion classes. In comparison to other states of methods, the Mini exception technique utilised in CNN layers acts as a lightweight system. The proposed solution has a 92% accuracy rate and removes the barrier between the current frameworks. The suggested music is taken from a music database and then further mapped in accordance with the algorithm's output.

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