Facial emotion recognition-based music recommendation system

https://doi.org/10.53730/ijhs.v6nS4.9419

Authors

  • Parashakthi. M Assistant Professor, Department of Information Technology, Dhanalakshmi Srinivasan College of Engineering and Technology
  • Savithri S Assistant Professor, Dhanalakshmi Srinivasan College of Engineering and Technology

Keywords:

recognition, artificial intelligence, openCV application

Abstract

Face recognition technology has gotten a lot of press because of its wide range of applications and market possibilities. It is used in a variety of fields, including security systems, digital video processing, and other technical advancements. Additionally, music is a form of art that is believed to have a stronger emotional connection It has a special power to improve one's mood. This research focuses on developing an effective music recommendation system that employs Facial Recognition algorithms to assess the user's sentiment. The constructed algorithm would prove to be more effective than previous systems. Furthermore, on a wider scale, this would allow for the recovery of time and labour spent physically performing the process. The system's overall goal is to recognise facial emotion and quickly recommend tunes. Both time and money will be saved with the proposed system.

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References

Bhat, A. S., Amith, V. S., Prasad, N. S., & Mohan, M. (2014). An Efficient Classification Algorithm For Music Mood Detection In Western and Hindi Music Using Audio Feature Extraction. 2014 Fifth International Conference on Signal and Image Processing, 359- 364. https://doi.org/10.1109/ICSIP.2014.63

Chew, L. W., Seng, K. P., Ang, L. M., Ramakonar, V., & Gnanasegaran, A. (2011), Audio-Emotion Recognition System using Parallel Classifiers and Audio Feature Analyzer. In Proceedings of the 2011 Third International Conference on Computational Intelligence, Modelling & Simulation (pp. 210-215), USA. IEEE. https://doi.org/10.1109/ CIMSim.2011.44

Deny, J., & Sundararajan, M. (2014). Survey of Texture Analysis UsingHistogram in Image Processing. International Journal of Applied Engineering Research, 9(26), 8737- 8739. https://www.researchgate.net/publication/304091674_Survey_of_Texture_ Analysis_Using_Histogram_in_Image_Processing

Deny, J., & Sundhararajan, M. (2015). Multi Modal Biometric Security for MANET Military Application-Face and Fingerprint. Journal of Computational and Theoretical Nanoscience, 12(12), 5949-5953. https://www.researchgate.net/ publication/304380954_Multi_Modal_Biometric_Security_for_MANET_ Military_Application-Face_and_Fingerprint

Deny, J., Muthukumaran, E., Ramkumar, S., & Kartheesawaran, S. (2018). Extraction Of Respiratory Signals And Motion Artifacts From PPG Signal Using Modified Multi Scale Principal Component Analysis. International Journal of Pure and Applied Mathematics, 119(12), 13719-13727. https://www.researchgate. net/publication/325465991_Extraction_Of_Respiratory_Signals_And_ Motion_Artifacts_From_PPG_Signal_Using_Modified_Multi_Scale_Principal_ Component_Analysis

Dureha, A. (2014). An Accurate Algorithm for Generating a Music laylist based on Facial Expressions. International Journal of Computer Applications, 100(9). https://pdfs. semanticscholar.org/312b/2566e315dd6e65bd42cfcbe4d919159de8a1.pdf 270 http://doi.org/10.17993/3ctecno.2020.specialissue4.261-271 3C Tecnología.

Gilda, S., Zafar, H., Soni, C., & Waghurdekar, K. (2017). Smart Music Player Integrating Facial Emotion Recognition and Music Mood Recommendation. In 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India. IEEE. https://doi.org/10.1109/WiSPNET.2017.8299738

Glosas de innovación aplicadas a la pyme. ISSN: 2254 – 4143 Edición Especial Special Issue Marzo 2020 Fan, X., Zhang, F., Wang, H., & Lu, X. (2012). The System of Face Detection Based on OpenCV. In 24th Chinese Control and Decision Conference (CCDC), Taiyuan, China. IEEE. https://doi.org/10.1109/CCDC.2012.6242980

Gossi, D., & Gunes, M. H. (2016). Lyric-based music recommendation. In Cherifi H., Gonçalves B., Menezes R., Sinatra R. (eds.) Complex Networks VII. Studies in Computational Intelligence, vol. 644. Springer, Cham. https://doi.org/10.1007/978-3- 319-30569-1_23

Gupta, S. (2018). Facial emotion recognition in real-time and static images. In 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, India. IEEE. https://doi.org/10.1109/ICISC.2018.8398861

Knyazev, B., Shvetsov, R., Efremova, N., & Kuharenko, A. (2018). Leveraging large face recognition data foremotion classification. In 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi’an, China. IEEE. https://doi. org/10.1109/FG.2018.00109

Kusumawati, A. H., Wulan, I. R., & Ridwanuloh, D. (2020). Formulation and physical evaluation sheet mask from red rice (Oryza Nivara) and virgin coconut oil (Cocos Nucifera L). International Journal of Health & Medical Sciences, 3(1), 60-64. https://doi.org/10.31295/ijhms.v3n1.148

Levi, G., & Hassner, T. (2011). Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns. In Proceedings ACM International Conference on Multimodal Interaction (ICMI), Seattle. https://doi.org/10.1145/2818346.2830587

Muthukumaran, E., Deny, J., Perumal, B., Suseendran, G., & Akila, D. (2015). A narrative Non-Invasive Diagnostic loom Based by the side of correlation of Nasal set Rhythm in addition to customary Three Radial Pulses Measurement. Journal of Physics: Conference Series, 1228(1). https://iopscience.iop.org/ article/10.1088/1742-6596/1228/1/012075 271 http://doi.org/10.17993/3ctecno.2020.specialissue4.261-271 3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254 – 4143 Edición Especial Special Issue Marzo 2020

Sankaranarayanan, S., & Deny, J. (2013). Securable Image Compression Using SPIHT Algorithm. International journal of Electronics and Communication Engineering & Technology, 4(4), 96-100. https://fdocuments.in/document/securable-image-compressionusing-spiht-algorithm.html

Tzanetakisand, G., & Cook, P. (2002). Musical Genre Classification of Audio Signals. IEEE Transactions on Speech and Audio Processing, 10(5). https://doi.org/10.1109/ TSA.2002.800560

Widana, I.K., Sumetri, N.W., Sutapa, I.K., Suryasa, W. (2021). Anthropometric measures for better cardiovascular and musculoskeletal health. Computer Applications in Engineering Education, 29(3), 550–561. https://doi.org/10.1002/cae.22202

Youssif, A. A. A., & Wesam, A. A. A. (2011). Automatic Facial Expression Recognition System based on Geometric and Appearance Features. Computer and Information Science, 4(2), 115-124. https://doi.org/10.5539/cis.v4n2p115

Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009). A survey of affect recognition methods: Audio, visual, and spontaneousexpressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(1). https://doi.org/10.1109/TPAMI.2008.52.

Published

21-06-2022

How to Cite

Parashakthi, M., & Savithri, S. (2022). Facial emotion recognition-based music recommendation system. International Journal of Health Sciences, 6(S4), 5829–5835. https://doi.org/10.53730/ijhs.v6nS4.9419

Issue

Section

Peer Review Articles