Facial Expression Detection & Music Player

References

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@article{1711869,
      author = {Amruta Amune, Chaitanya Rankhamb, Samadhan Rathod, Akshay Sabbenwad, Pratyunsh Katkar},
      title = {Facial Expression Detection & Music Player},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {702-708},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711869.pdf},
      abstract = {Facial expression detection is revolutionizing music players by using AI and machine learning to recognize emotions and play mood-based songs automatically. Deep learning techniques like Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) effectively analyse facial features using methods such as Histogram of Oriented Gradients (HOG), Principal Component Analysis (PCA), and Haar Cascade classifiers. Some systems even integrate heart rate analysis for improved accuracy. Music recommendation varies from fixed playlists to AI-driven, real-time suggestions, incorporating sentiment analysis and environmental factors for enhanced personalization. However, challenges like real-time processing, lighting conditions, and data privacy persist. Future advancements focus on optimizing models for mobile devices, integrating multiple data sources, and improving user feedback mechanisms. By bridging emotions with technology, these systems aim to create a seamless and engaging music experience.},
      keywords = {Facial Expression Detection, Emotion-Based Music Player, AI Music Recommendation, Machine Learning  in Music, Deep Learning for Emotion Recognition},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1711869}
  }