Programs
- M. Tech. in Automotive Engineering -Postgraduate
- Fellowship Program in Paediatric and Congenital Heart Surgery -Fellowship
Publication Type : Conference Proceedings
Publisher : Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019
Source : Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019. Institute of Electrical and Electronics Engineers Inc., Melmaruvathur; India, pp. 400-404, 2019
ISBN : 9781538675953
Keywords : Accidents, Classification (of information), Deep learning, Drunk, Face recognition, Feature vectors, Fisher linear discriminants, Gaussian distribution, image classification, Recognition, Sober
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Verified : No
Year : 2019
Abstract : A major share of accidents happening today is categorised under drunk and drive accidents. Attempts to curb these accidents are limited to manual checking of drivers and awareness programs, which is evidently not enough or stringent. We propose a system where the driver's face is captured in thermal image spectrum and is first recognised using facial recognition, then classified as drunk or sober. The former is done using a deep learning tool that is Convolution Neural Network and the latter is done using Gaussian Mixture Model along with Fischer Linear Discriminant for dimensionality reduction. Post the facial recognition, we will be using capillary junction points on faces to determine difference in blood temperature thus allowing us to classify them as drunk or not. © 2019 IEEE.
Cite this Research Publication : S. Menon, Swathi, J., Anit, S. K., Nair, A. P., and Sarath S., “Driver face recognition and sober drunk classification using thermal images”, Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019. Institute of Electrical and Electronics Engineers Inc., Melmaruvathur; India, pp. 400-404, 2019