Human Mood Detection using Image Processing and Machine Learning and Deep Learning
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This work aims to develop an efficient algorithm to automatic the detection of emotions based on facial expressions. Which involves the use of computer vision & machine learning techniques to classify the emotions of individuals or groupsin realtime using an image. As mood detection refers to the process of using various techniques and tools to identify or recognize the emotional state or mood of an individual based on their facial expressions. Its purpose will be to provide insights into the psychological state of individuals for various applications such as mental health diagnosis. It typically involves the use of machine learning algorithms and natural language processing techniques to analyze and interpret human behavior. This approach also uses deep learning models to learn the features of facial expressions and detect the corresponding emotions. The results show that the proposed algorithm is accurately detecting emotions from images with better accuracy & less false detection, which can be suitable for use in various applications such as healthcare, entertainment and social media.
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P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, Kauai, HI, USA, 2001, pp. I-I, DOI: https://doi.org/10.1109/CVPR.2001.990517
Caifeng S, Gong S, McOwan P, Facial expression recognition based on Local Binary Patterns, Image and Vision Computing, Volume 27, Issue 6, 4 May 2009, Pages 803-816, DOI: https://doi.org/10.1016/j.imavis.2008.08.005
Shah, Parin M., "Face Detection from Images Using Support Vector Machine" (2012). Master's Projects. 321. DOI: https://doi.org/10.31979/etd.wg5s-gyqn
Y. Seanglidet, B. S. Lee and C. K. Yeo, "Mood prediction from facial video with music “therapy” on a smartphone," 2016 Wireless Telecommunications Symposium (WTS), London, UK, 2016, pp. 1-5, DOI: https://doi.org/10.1109/WTS.2016.7482034
Y. Taigman, M. Yang, M. Ranzato and L. Wolf, "DeepFace: Closing the Gap to Human-Level Performance in Face Verification," 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 1701-1708, DOI: https://doi.org/10.1109/CVPR.2014.220
O. Barkan, J. Weill, L. Wolf and H. Aronowitz, "Fast High Dimensional Vector Multiplication Face Recognition," 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia, 2013, pp. 1960-1967, DOI: https://doi.org/10.1109/ICCV.2013.246
Berg, Thomas, and Peter N. Belhumeur. "Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification." https://pdfs.semanticscholar.org/5f14/a9595b0796ce6e5338f157b763326c1f632f.pdf
L. Wolf, The SVM-minus similarity score for video face recognition, CVPR2013, IEEE Explore. DOI: https://doi.org/10.1109/CVPR.2013.452
Y. Sun, X. Wang and X. Tang, "Deep Convolutional Network Cascade for Facial Point Detection," 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013, pp. 3476-3483, DOI: https://doi.org/10.1109/CVPR.2013.446
Phillips, P., Beveridge, J., Draper, B., Givens, G., O'Toole, A., Bolme, D., Dunlop, J., Lui, Y., Sahibzada, H. and Weimer, S. (2011), An Introduction to the Good, the Bad, & the Ugly Face Recognition Challenge Problem, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], DOI: https://doi.org/10.6028/NIST.IR.7758
Riyazuddin, Y. Md., Basha, S. M., Reddy, K. K., & Banu, S. N. (2020). Effective Usage of Support Vector Machine in Face Detection. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 3, pp. 1336–1340). DOI: https://doi.org/10.35940/ijeat.C5406.029320
Sharma, P., & Chauhan, N. (2020). Spoofing Face Detection using LBP Descriptor and KNN Classifier in Image Processing. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 5, pp. 2281–2286). DOI: https://doi.org/10.35940/ijrte.E5781.018520
Bhavana, Naveen, V. J., & Kishore, K. K. (2019). Comprehensive Analysis of Machine Learning Algorithms for Face Detection. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 10, pp. 2479–2482). DOI: https://doi.org/10.35940/ijitee.J9542.0881019
Kumari, J., Patidar, K., Saxena, Mr. G., & Kushwaha, Mr. R. (2021). A Hybrid Enhanced Real-Time Face Recognition Model using Machine Learning Method with Dimension Reduction. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 1, Issue 3, pp. 12–16). DOI: https://doi.org/10.54105/ijainn.B1027.061321
Sharma, Dr. K., & Garg, N. (2021). An Extensive Review on Image Segmentation Techniques. In Indian Journal of Image Processing and Recognition (Vol. 1, Issue 2, pp. 1–5). DOI: https://doi.org/10.54105/ijipr.B1002.061221