Real-Time Emotion Detection and Recommendation System

References

[1] S. Bandyopadhyay, R. Das, and P. Sharma, “Emotion recognition using deep learning techniques,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 4, pp. 112–118, 2022.

[2] K. Aparna, S. Reddy, and P. Nair, “Real-time facial emotion detection system using machine learning,” International Journal of Innovative Research in Technology, vol. 10, no. 2, pp. 45–50, 2023.

[3] A. Ghorpade, R. Patil, and S. Kulkarni, “CNN-based emotion recognition from facial expressions,” International Journal of Computer Vision and Image Processing, vol. 14, no. 1, pp. 25–33, 2023.

[4] S. Dubey, M. Tiwari, and A. Verma, “Facial expression recognition using artificial intelligence,” Journal of Computer Engineering and Applications, vol. 9, no. 3, pp. 78–84, 2023.

[5] R. Singh and A. Kumar, “Emotion-aware intelligent recommendation systems,” International Journal of Smart Computing and Artificial Intelligence, vol. 11, no. 1, pp. 90–97, 2024.

[6] P. Ekman, Handbook of Cognition and Emotion. New York, NY, USA: Wiley, 1999.

[7] P. Nalini, “Machine learning approaches for facial emotion analysis,” International Journal of Emerging Technologies, vol. 8, no. 2, pp. 66–72, 2022.

[8] A. Parihar, “Advanced deep learning models for emotion classification,” International Journal of AI Research, vol. 5, no. 1, pp. 12–19, 2025.

[9] S. M. Mohammad and R. E. Banchs, “Emotion recognition and sentiment analysis across multiple domains,” Proceedings of the International Conference on Affective Computing, pp. 120–126, 2021.

[10] I. J. Goodfellow et al., “Challenges in representation learning: A report on three machine learning contests,” Neural Networks, vol. 64, pp. 59–63, 2015. (FER-2013 Dataset) :contentReference[oaicite:1]{index=1}

[11] R. Halder et al., “Emotion recognition using machine learning and image processing,” International Journal of Computer Applications, vol. 175, no. 8, pp. 1–6, 2020.

[12] G. Chanel, J. Kronegg, D. Grandjean, and T. Pun, “Emotion assessment using physiological signals and EEG,” in Proc. Int. Workshop Multimedia Content Representation, Classification and Security, 2006, pp. 530–537.

[13] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, 2001, pp. I–511–I–518. :contentReference[oaicite:2]{index=2}

[14] S. Raschka and V. Mirjalili, Python Machine Learning, 3rd ed. Birmingham, U.K.: Packt Publishing, 2019.

@article{1715945,
      author = {Srushti Jadhav, Mrudul More, Abdul Mongal, Manik Patil, Rashmi More},
      title = {Real-Time Emotion Detection and Recommendation System},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {263-267},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715945.pdf},
      abstract = {Emotion recognition plays a crucial role in enhancing human–computer interaction by enabling systems to understand and respond to user emotions effectively. This paper presents a real-time facial emotion detection and intelligent recommendation system using deep learning techniques. The proposed system utilizes a Convolutional Neural Network (CNN) trained on grayscale facial images to classify emotions into four categories: Angry, Happy, Neutral, and Sad. The model is trained on a balanced dataset to ensure unbiased learning across all classes. Extensive preprocessing and data handling techniques are applied to improve model generalization. The system captures real-time video input, detects facial features, and predicts emotions dynamically, followed by generating appropriate recommendations based on the detected emotional state. Experimental results demonstrate a significant improvement in model performance, achieving an accuracy of 75%, compared to an earlier baseline of 68%, highlighting the effectiveness of the proposed approach. The system is efficient, scalable, and suitable for real-world applications such as mental health monitoring, personalized user interaction, and smart assistive systems},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1715945}
  }