Skip to main content

Emotion Analysis to Provide Counseling to Students Fighting from Depression and Anxiety by Using CCTV Surveillance

  • Conference paper
  • First Online:
Machine Learning and Information Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1101))

Abstract

Around 18.5% of the Indian population of students suffer from depression and around 24.4% of students suffer from anxiety disorder. Depression and anxiety are treatable through counseling and certain medicines, and thus, to avail to this huge percentage of students, the help that they require is provided in many reputed colleges and universities. These colleges and universities hire professional counselors to cater to the needs of these students. But, as all problems are not easy to overcome, in this situation also, there is a huge problem. That problem is of students not venting out their need for counseling due to various reasons, and hence, they do not go to counselors to get themselves back in happy life. To conquer such problems, a solution is proposed in this paper, that is, the use of CCTV surveillance recording that is now readily available in various colleges and universities, along with sentiment analysis of each and every student. Their emotional well-being will be monitored through their facial landmark recognition, and if certain students are showing signs of depression through his or her activities, then their names are given to their respective counselors, so as to provide them with care and support and right guidance to start their life afresh. This paper makes use of computer vision, image processing, and convolutional neural network to complete the above-mentioned task.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Balasubramanian, S. 2017. #LetsTalk, Health and Life, Mental Health, Taboos: ‘I didn’t want to Say I’m Depressed. Help Me.’ A Student Opens Up. https://www.youthkiawaaz.com/2017/04/depression-college-students-india.

  2. Xu, Z., Y. Jiang, Y. Wang, et al. 2019. Local polynomial contrast binary patterns for face recognition. Neurocomputing 355: 1–12.

    Google Scholar 

  3. Revina, I.M., and W.R.S. Emmanuel. 2018. A survey on human face expression recognition techniques. Journal of King Saud University-Computer and Information Sciences 1–8.

    Google Scholar 

  4. Ahmed, A., J. Guo, F. Ali, et al. 2018. LBPH based improved face recognition at low resolution. In 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), 144–147.

    Google Scholar 

  5. Byeon, Y.H., and K.C. Kwak. 2014. Facial expression recognition using 3D convolutional neural network. International Journal of Advanced Computer Science and Applications (IJACSA) 5 (12): 107–112.

    Google Scholar 

  6. Dubey, M., and P.L. Singh. 2016. Automatic emotion recognition using facial expression: a review. International Research Journal of Engineering and Technology 03 (02): 488–492.

    Google Scholar 

  7. Dang, L.M., S.I. Hassan, et al. 2019. Face image manipulation detection based on a convolutional neural network. Expert Systems with Applications 129: 156–168.

    Google Scholar 

  8. Viola, P., and M. Jones. 2001. Rapid object detection using a boosted cascade of simple features. Accepted Conference on Computer Vision and Pattern Recognition.

    Google Scholar 

  9. Park, K.Y., and S.N. Hwang. 2014. An improved Haar-like feature for efficient object detection. Pattern Recognition Letters 42: 148–153.

    Article  Google Scholar 

  10. Badgerati. 2010. Computer vision—the integral image. https://computersciencesource.wordpress.com/2010/09/03/computer-vision-the-integral-image/.

  11. Chai, S., B. Kisačanin, and N. Bellas. 2010. Special issue on embedded vision. Computer Vision and Image Understanding 114 (11): 1115–1316.

    Google Scholar 

  12. Kun, B., L.L. Zhao, L. Fang, and S. Lian. 2012. 3D face recognition method based on cascade classifier. Procedia Engineering 29: 705–709.

    Google Scholar 

  13. Khan, R.A., A. Meyer, H. Konik, et al. 2013. Framework for reliable, realtime facial expression recognition for low resolution images. Pattern Recognition Letters 34 (10): 1159–1168.

    Article  Google Scholar 

  14. Lv, C., Z. Wu, X. Wang, and M. Zhou. 2019. 3D facial expression modeling based on facial landmarks in single image. Neurocomputing 355: 155–167.

    Article  Google Scholar 

  15. Babu, D.R., R.S. Shankar, G. Mahesh, et al. 2017. Facial expression recognition using Bezier curves with hausdorff distance. In 2017 International Conference on IoT and Application (ICIOT), 1–8.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Saurabh Bilgaiyan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sinha, S., Mishra, S.K., Bilgaiyan, S. (2020). Emotion Analysis to Provide Counseling to Students Fighting from Depression and Anxiety by Using CCTV Surveillance. In: Swain, D., Pattnaik, P., Gupta, P. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1884-3_8

Download citation

Publish with us

Policies and ethics