Driver Emotional Status Recognition Using Artificial Neural Network

Authors(2) :-Mohan Arava, Prudhvi Ravi Raja Reddy Mallidi

Artificial neural network is one of the fascinating area of study, the proposed architecture is performs better feature extraction than earlier proposed Fuzzy logic and SVM algorithms. Day to day the automotive industries are actively supporting research and innovations related to safety issues, performance, and environment. A driver status assessment system constructed in two different modules: one is for driver fatigue detection based on captured images through Digital cameras. The fatigue is a percentage of eyes closure of the best indicator of fatigue for vision systems. And the second one is driver distraction system by using head, facial expressions, and body, a fusion strategy is to deduce the type's driver distractions. Of course many aspects are there but these two fatigue and distraction are only a fraction of all possible drivers' states of their dramatic impact on traffic safety.The standard databases are used for quantitative evaluation of the current state of the art approach using ANN achieves better accuracy then compared to Fuzzy and SVM. The proposed architecture endorses the efficiency and reliable usage of the work for real world applications.

Authors and Affiliations

Mohan Arava
Assistant Professor, Department of IT, Aditya College of Engineering & Technology, Surampalem, Kakinada, East Godavari, Andhra Pradesh, India
Prudhvi Ravi Raja Reddy Mallidi
Assistant Professor, Department of CSE, Aditya College of Engineering & Technology, Surampalem, Kakinada, East Godavari, Andhra Pradesh, India

Artificial Neural Network, SVM Algorithms, Fuzzy Logic, GoogleNet, AAM

  1. K. Anderson and P. W. Mcowan, "A real-time automated system for recognition of human facial expressions," IEEE Trans. Syst.,Man, Cybern. B, Cybern, pp. 96-105, 2006.
  2. I. Kotsia and I. Pitas, "Facial expression recognition in image sequences using geometric deformation features and support vector machines," Image Processing, IEEE Transactions on, vol. 16, no. 1, pp. 172-187, Jan 2007.
  3. B. V. Kumar, "Face expression recognition and analysis: the state of the art," Course Paper, Visual Interfaces to Computer, 2009.
  4. S. Happy and A. Routray, "Automatic facial expression recognition using features of salient facial patches," Affective Computing,IEEE Transactions on, vol. 6, no. 1, pp. 1-12, Jan 2015.
  5. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, "Decaf: A deep convolutional activation feature for generic visual recognition," arXiv preprint arXiv:1310.1531, 2013.
  6. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," CoRR, vol. abs/1409.4842, 2014. Online]. Available:
  7. LSVRC. (2014, Jun.) Results of the lsvrc challenge. Online]. Available: results
  8. C.-D. Caleanu, "Face expression recognition: A brief overview of the last decade," in Applied Computational Intelligence and Informatics (SACI), 2013 IEEE 8th International Symposium on. IEEE, 2013, pp. 157-161.
  9. V. Bettadapura, "Face expression recognition and analysis: the state of the art," arXiv preprint arXiv:1203.6722, 2012.
  10. "SVM - Support Vector Machines." Online]. Available: Accessed: 25-May-2016].
  11. C. J. C. J. C. J. C. C. J. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Min. Knowl. Discov., vol. 2, no. 2, pp. 121-167, 1998.
  12. M. Riedmiller and H. Braun, "A direct adaptive method for faster backpropagation learning: The RPROP algorithm," IEEE Int. Conf. Neural Networks - Conf. Proc., vol. 1993-January, pp. 586-591, 1993.
  13. M. T. Hagan and M. B. Menhaj, "Training Feedforward Networks with the Marquardt Algorithm," IEEE Trans. Neural Networks, vol. 5, no. 6, pp. 989-993, 1994.

Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1181-1185
Manuscript Number : CSEIT1726329
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Mohan Arava, Prudhvi Ravi Raja Reddy Mallidi , "Driver Emotional Status Recognition Using Artificial Neural Network", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.1181-1185 , November-December-2017.
Journal URL :

Article Preview