Human Emotion Surveillance Using Computer Vision

  • Authors

    • Dr. R.Radha
    • Atchatha. M
    • Kaushik. B
    • Agassi Felix A
    • G. Staflin Betzy
    2018-09-25
    https://doi.org/10.14419/ijet.v7i4.6.20223
  • Facial recognition, viola-jones algorithm, weak classifier.

  • India, a land of marvels, is outstanding in many aspects, its culture, ecosystem, etc. Sadly, it also ranks among the top countries in the world to have an annual suicide rate. This project aims at the foundation of human emotion surveillance.  This system assists in the facial recognition, feature extraction and the threshold detection of stress for emotions expressed through face using the viola-jones algorithms and weak classifiers.  This focuses basically on segregation of positive and negative emotions, detecting stress based on a usual threshold value and possibly providing an alternate means to let loose the extra stress built up if possible.

     

     

  • References

    1. [1] Dr. Margaret Chan, “Preventing suicide a global imperativeâ€, World Health Organization, (2014).

      [2] Rajiv Radhakrishnan, Chittaranjan, “Suicide: An Indian perspectiveâ€, Indian Journal of Psychiatry, (2012).

      [3] Soman C, Vijayakumar K, Ajayan K, Safraj S, Kutty V, “Suicide in South India: a community-based study in Keralaâ€, Indian J Psychiatry, (2009), Vol.51, pp.261-264.

      [4] Deb, Esben Strodl, Jiandong Sun, “Academic Stress, Parental Pressure, Anxiety and Mental Health among Indian High School Studentsâ€, International Journal of Psychology and Behavioral Science, (2015), Vol.5, Issue.1, pp.26-34.

      [5] Vikram Patel, Chinthanie Ramasundarahettige, Lakshmi Vijayakumar, J S Thakur, Vendhan Gajalakshmi, Gopalkrishna Gururaj, Wilson Suraweera, Prabhat Jha, “Suicide mortality in India: a nationally representative surveyâ€, The Lancet, (2012), Vol.379, Issue. 9834, pp.2343–2351.

      [6] P. Viola and M. Jones, “Rapid object detection was using a boosted cascade of simple featuresâ€, CVPR, (2001), pp.511–518.

      [7] Damir Filko, Goran Martinovi´c, “Emotion Recognition System by a Neural Network Based Facial Expression Analysisâ€, Automatika, (2013), Vol.54, Issue.2, pp 263–272.

      [8] Neha Gupta1 and Navneet Kaur, “Design and Implementation of Emotion Recognition System by Using Matlabâ€, IJERA, (2013), Vol.3, Issue 4, pp.2002-2006.

      [9] Seyedehsamaneh Shojaeilangari, Wei-Yun Yau, Karthik Nandakumar, Li Jun, and Eam Khwang Teoh, “Robust Representation and Recognition of Facial Emotions Using Extreme Sparse Learningâ€, IEEE Transactions on Image Processing, (2015), Vol.24, No.7, pp.2140-2153.

      [10] Bosker, Bianca, “AFFECTIVA’s Emotion Recognition Tech: When Machines Know what you’re feelingâ€, (2013).

      [11] Vikramjit Mitra1etal, “Cross-Corpus Depression Prediction From Speechâ€, ICASSP, (2015), pp.4769-4773.

      [12] Fuji Ren, Xin Kang, and Changqin Quan “Examining Accumulated Emotional Traits in Suicide Blogs with an Emotion Topic Model†IEEE Journal Of Biomedical And Health Informatics, (2016), Vol.20, Issue.5, pp.1348-1351.

      [13] Mohammad Soleymani, Sadjad Asghari-Esfeden, Yun Fu, Maja Pantic, “Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detectionâ€, IEEE Transactions On Affective Computing, (2016), Vol.7, Issue.1, pp.17-28.

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  • How to Cite

    R.Radha, D., M, A., B, K., Felix A, A., & Staflin Betzy, G. (2018). Human Emotion Surveillance Using Computer Vision. International Journal of Engineering & Technology, 7(4.6), 9-12. https://doi.org/10.14419/ijet.v7i4.6.20223