Face Expression Recognition using CNN

Authors

  • Ashna Namira  Computer Science Engineering, Srinivas Institute of Technology, Mangalore, Karnataka, India
  • Aravind Naik  Computer Science Engineering, Srinivas Institute of Technology, Mangalore, Karnataka, India
  • Nikhil Floyd Dsouza  Computer Science Engineering, Srinivas Institute of Technology, Mangalore, Karnataka, India

DOI:

https://doi.org/10.32628/CSEIT217451

Keywords:

Inductive Learning Algorithm (ILA), XCeption Algorithm

Abstract

The emotions evolved in face have an excellent influence on decisions and arguments about various subjects. In psychological theory, emotional states of an individual are often classified into six main categories: surprise, fear, disgust, anger, happiness and sadness. Automatic extraction of those emotions from the face images can help in human computer interaction also as many other applications. Machine learning algorithms and particularly deep neural network can learn complex features and classify the extracted patterns. In this paper, a deep learning¬based framework is used for human emotion recognition. The proposed framework uses the feature extraction then a Convolutional Neural Network (CNN) for classification. The experimental results show that the proposed methodology increases both of the speed training process of CNN and therefore the recognition accuracy.

References

  1. Dolan, "The effects of aquarium size and temperature on color vibrancy size and physical activity in bettasplendens", 2015.
  2. S. Sharpe, Aquarium Nitrogen Cycle: How an Aquarium Cycles, 2018, onlineAvailable: https://www.thesprucepets.com/aquarium-nitrogen-cycle-1378370.
  3. C. Dupont, P. Cousin and S. Dupont, "IoT for aquaculture 4.0 smart and easy-to-deploy real-time water monitoring with IoT", Proc. Global Internet Things Summit (GIoTS), pp. 1-5, Jun. 2018.
  4. T. I. Salim, T. Haiyunnisa and H. S. Alam, "Design and implementation of water quality monitoring for eel fish aquaculture an examination of microbubble aeration", Proc. Int. Symp. Electron. Smart Devices (ISESD), pp. 29-30, Nov. 2016.
  5. K. M. Stehfest, C. G. Carter, J. D. McAllister, J. D. Ross and J. M. Semmens, "Response of atlantic salmon salmo salar to temperature and dissolved oxygen extremes established using animal-borne environmental sensors", Nature, vol. 7, pp. 4545, 2017.
  6. J.-H. Chen, W.-T. Sung and G.-Y. Lin, "Automated monitoring system for the fish farm aquaculture environment", Proc. IEEE Int. Conf. Syst. Man Cybern., pp. 1161-1166, Oct. 2015.
  7. C. Encinas, E. Ruiz, J. Cortez and A. Espinoza, "Design and implementation of a distributed IoT system for the monitoring of water quality in aquaculture", Proc. Wireless Telecommun. Symp. (WTS), pp. 1-7, Apr. 2017.
  8. Y.-B. Lin, Y.-W. Lin, C.-M. Huang, C.-Y. Chih and P. Lin, "IoTtalk: A management platform for reconfigurable sensor devices", IEEE Internet Things J., vol. 4, no. 5, pp. 1152-1562, Oct. 2017.
  9. G. Farmer, CO2: Striking the Balance, 2016, onlineAvailable: https://www.practicalfishkeeping.co.uk/features/articles/co2-striking-the-balance.
  10. J. E. Bly and W. L. Clem, "Temperature and teleost immune functions", Fish Shellfish Immunol., vol. 2, no. 3, pp. 159-171, 1992.
  11. T. Masaharu et al., "Effect of temperature on survival growth and malformation of cultured larvae and juveniles of the seven-band grouper epinephelus septemfasciatus", Fisheries Sci., vol. 80, no. 1, pp. 69-81, 2014.
  12. The Importance of Oxygen in the Aquarium, 2018, onlineAvailable: https://www.algone.com/oxygen-in-the-aquarium.
  13. Your Guide to Ammonia Toxicity, 2011, onlineAvailable: http://www.aquariumadvice.com/forums/f12/your-guide-to-ammonia-toxicity-159994.html.
  14. T. Sarac, Electrical Conductivity in Freshwater Aquariums, 2018, onlineAvailable: http://fluvalaquatics.com/ca/explore/did-you-know/equipment/155-electrical-conductivity-monitoring-system-part-two/#.XAL_hzgzZ0x.
  15. What You Need to Know About Reverse Osmosis, 2016,onlineAvailable: https://www.practicalfishkeeping.co.uk/features/articles/what-you-need-to-know-about-reverse-osmosis.

Downloads

Published

2021-08-30

Issue

Section

Research Articles

How to Cite

[1]
Ashna Namira, Aravind Naik, Nikhil Floyd Dsouza, " Face Expression Recognition using CNN" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 4, pp.235-240, July-August-2021. Available at doi : https://doi.org/10.32628/CSEIT217451