research-article

DIF : Dataset of Perceived Intoxicated Faces for Drunk Person Identification

Authors:

Sai Srinadhu Katta,

Devendra Pratap Yadav,

Abhinav DhallAuthors Info & Claims

ICMI '19: 2019 International Conference on Multimodal Interaction

Pages 367 - 374

https://doi.org/10.1145/3340555.3353754

Published: 14 October 2019 Publication History

Abstract

Traffic accidents cause over a million deaths every year, of which a large fraction is attributed to drunk driving. An automated intoxicated driver detection system in vehicles will be useful in reducing accidents and related financial costs. Existing solutions require special equipment such as electrocardiogram, infrared cameras or breathalyzers. In this work, we propose a new dataset called DIF (Dataset of perceived Intoxicated Faces) which contains audio-visual data of intoxicated and sober people obtained from online sources. To the best of our knowledge, this is the first work for automatic bimodal non-invasive intoxication detection. Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) are trained for computing the video and audio baselines, respectively. 3D CNN is used to exploit the Spatio-temporal changes in the video. A simple variation of the traditional 3D convolution block is proposed based on inducing non-linearity between the spatial and temporal channels. Extensive experiments are performed to validate the approach and baselines.

References

[1]

[1] Driver assistance systems for urban areas.2018. www.bosch-mobility-solutions.com/en/highlights/automated-mobility/driver-assistance-systems-for-urban-areas.

[2]

Tadas Baltrusaitis, Peter Robinson, and Louis-Philippe Morency. 2013. Constrained local neural fields for robust facial landmark detection in the wild. In Computer Vision Workshops (ICCVW), 2013 IEEE International Conference on. IEEE, 354–361.

Digital Library

[3]

Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. 2016. Openface: an open source facial behavior analysis toolkit. In Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on. IEEE, 1–10.

[4]

Eva Susanne Capito, Stefan Lautenbacher, and Claudia Horn-Hofmann. 2017. Acute alcohol effects on facial expressions of emotions in social drinkers: a systematic review. Psychology research and behavior management 10 (2017), 369.

[5]

Ciprian Adrian Corneanu, Marc Oliu Simón, Jeffrey F Cohn, and Sergio Escalera Guerrero. 2016. Survey on rgb, 3d, thermal, and multimodal approaches for facial expression recognition: History, trends, and affect-related applications. IEEE transactions on pattern analysis and machine intelligence 38, 8(2016), 1548–1568.

[6]

Abhinav Dhall, Roland Goecke, Jyoti Joshi, Jesse Hoey, and Tom Gedeon. 2016. Emotiw 2016: Video and group-level emotion recognition challenges. In Proceedings of the 18th ACM International Conference on Multimodal Interaction. ACM, 427–432.

Digital Library

[7]

[7] Drinking, Driving: A Road Safety Manual for Decision-Makers, and Practitioners.2007. Global Road Safety Partnership, Geneva, Switzerland.

[8]

Samira Ebrahimi Kahou, Vincent Michalski, Kishore Konda, Roland Memisevic, and Christopher Pal. 2015. Recurrent neural networks for emotion recognition in video. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction. ACM, 467–474.

Digital Library

[9]

Paul Ekman and Wallace V Friesen. 1978. Manual for the facial action coding system. Consulting Psychologists Press.

[10]

Rana El Kaliouby and Peter Robinson. 2005. Real-time inference of complex mental states from facial expressions and head gestures. In Real-time vision for human-computer interaction. Springer, 181–200.

[11]

Florian Eyben, Martin Wöllmer, and Björn Schuller. 2010. Opensmile: the munich versatile and fast open-source audio feature extractor. In Proceedings of the 18th ACM international conference on Multimedia. ACM, 1459–1462.

Digital Library

[12]

Yin Fan, Xiangju Lu, Dian Li, and Yuanliu Liu. 2016. Video-based emotion recognition using CNN-RNN and C3D hybrid networks. In Proceedings of the 18th ACM International Conference on Multimodal Interaction. ACM, 445–450.

Digital Library

[13]

Carl-Magnus Ideström and Björn Cadenius. 1968. Time relations of the effects of alcohol compared to placebo. Psychopharmacologia 13, 3 (1968), 189–200.

[14]

Aditya Joshi, Abhijit Mishra, Balamurali AR, Pushpak Bhattacharyya, and Mark Carman. 2016. A computational approach to automatic prediction of drunk texting. arXiv preprint arXiv:1610.00879(2016).

[15]

Takeo Kanade, Jeffrey F Cohn, and Yingli Tian. 2000. Comprehensive database for facial expression analysis. In Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on. IEEE, 46–53.

Digital Library

[16]

Georgia Koukiou and Vassilis Anastassopoulos. 2012. Drunk person identification using thermal infrared images. International journal of electronic security and digital forensics 4, 4(2012), 229–243.

[17]

Shan Li, Weihong Deng, and JunPing Du. 2017. Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2584–2593.

[18]

Suman Kalyan Maity, Ankan Mullick, Surjya Ghosh, Anil Kumar, Sunny Dhamnani, Sudhansu Bahety, and Animesh Mukherjee. 2018. Understanding Psycholinguistic Behavior of predominant drunk texters in Social Media. In 2018 IEEE Symposium on Computers and Communications (ISCC). IEEE, 01096–01101.

[19]

[19] Driver Attention Monitor.2018. owners.honda.com/vehicles/information/2017/CR-V/features/Driver-Attention-Monitor.

[20]

Maja Pantic, Michel Valstar, Ron Rademaker, and Ludo Maat. 2005. Web-based database for facial expression analysis. In Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on. IEEE, 5–pp.

[21]

O. M. Parkhi, A. Vedaldi, and A. Zisserman. 2015. Deep Face Recognition. In British Machine Vision Conference.

[22]

[22] Pyannote-video.2018. https://www.github.com/pyannote/pyannote-video.

[23]

Florian Schiel, Christian Heinrich, Sabine Barfüsser, and Thomas Gilg. 2008. ALC: Alcohol Language Corpus. In LREC.

[24]

Björn Schuller, Stefan Steidl, Anton Batliner, Florian Schiel, and Jarek Krajewski. 2011. The INTERSPEECH 2011 speaker state challenge. In Twelfth Annual Conference of the International Speech Communication Association.

[25]

Jéssica Bruna Santana Silva, Eva Dias Cristino, Natalia Leandro de Almeida, Paloma Cavalcante Bezerra de Medeiros, and Natanael Antonio dos Santos. 2017. Effects of acute alcohol ingestion on eye movements and cognition: A double-blind, placebo-controlled study. PloS one 12, 10 (2017), e0186061.

[26]

[26] Global status report on road safety 2015.2015. World Health Organization: Geneva, Switzerland, 2015.

[27]

Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. 2015. Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE international conference on computer vision. 4489–4497.

Digital Library

[28]

MPEG Video. 1998. SNHC,“Text of ISO/IEC FDIS 14 496-3: Audio,”. Atlantic City MPEG Mtg(1998).

[29]

Erroll Wood, Tadas Baltrusaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling. 2015. Rendering of eyes for eye-shape registration and gaze estimation. In Proceedings of the IEEE International Conference on Computer Vision. 3756–3764.

Digital Library

[30]

Chung Kit Wu, Kim Fung Tsang, Hao Ran Chi, and Faan Hei Hung. 2016. A precise drunk driving detection using weighted kernel based on electrocardiogram. Sensors 16, 5 (2016), 659.

[31]

Guoying Zhao and Matti Pietikainen. 2007. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE transactions on pattern analysis and machine intelligence 29, 6(2007), 915–928.

Digital Library

Cited By

Islam RBae S(2024)FacePsy: An Open-Source Affective Mobile Sensing System - Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic SettingsProceedings of the ACM on Human-Computer Interaction10.1145/36765058:MHCI(1-32)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1145/3676505
Rahul Harikumar AGrover TKanchana M(2023)Detection of Intoxication in Automobile Drivers2023 Second International Conference on Electronics and Renewable Systems (ICEARS)10.1109/ICEARS56392.2023.10085153(366-373)Online publication date: 2-Mar-2023
https://doi.org/10.1109/ICEARS56392.2023.10085153
Vijay A(2023)Intelligent Driver Monitoring System for Safe Driving2023 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC)10.1109/ESARS-ITEC57127.2023.10114838(1-6)Online publication date: 29-Mar-2023
https://doi.org/10.1109/ESARS-ITEC57127.2023.10114838
Show More Cited By

Recommendations

A deep convolutional neural network model to classify heartbeats

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of ...
A novel deep learning framework for the identification of tortuous vessels in plus diseased infant retinal images

Retinopathy of prematurity (ROP), sometimes known as Terry syndrome, is an ophthalmic condition that affects premature babies. It is the main cause of childhood blindness and morbidity of vision throughout life. ROP frequently coexists with a ...
Cross-dataset person re-identification using deep convolutional neural networks: effects of context and domain adaptation

Over the past years, the impact of surveillance systems on public safety increases dramatically. One significant challenge in this domain is person re-identification, which aims to detect whether a person has already been captured by another camera in ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICMI '19: 2019 International Conference on Multimodal Interaction

October 2019

601 pages

ISBN:9781450368605

DOI:10.1145/3340555

Editors:
Wen Gao
Peking University, China
,
Helen Mei Ling Meng
Chinese University of Hong Kong, China
,
Matthew Turk
Toyota Technological Institute at Chicago, USA
,
Susan R. Fussell
Cornell University, USA
,
Björn Schuller
Imperial College London / University of Augsburg, UK
,
Yale Song
Microsoft Research, USA
,
Kai Yu
Shanghai Jiao Tong University, China

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 October 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICMI '19

ICMI '19: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

October 14 - 18, 2019

Suzhou, China

Acceptance Rates

Overall Acceptance Rate 453 of 1,080 submissions, 42%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
347
Total Downloads

Downloads (Last 12 months)59
Downloads (Last 6 weeks)3

Reflects downloads up to 20 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Islam RBae S(2024)FacePsy: An Open-Source Affective Mobile Sensing System - Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic SettingsProceedings of the ACM on Human-Computer Interaction10.1145/36765058:MHCI(1-32)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1145/3676505
Rahul Harikumar AGrover TKanchana M(2023)Detection of Intoxication in Automobile Drivers2023 Second International Conference on Electronics and Renewable Systems (ICEARS)10.1109/ICEARS56392.2023.10085153(366-373)Online publication date: 2-Mar-2023
https://doi.org/10.1109/ICEARS56392.2023.10085153
Vijay A(2023)Intelligent Driver Monitoring System for Safe Driving2023 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC)10.1109/ESARS-ITEC57127.2023.10114838(1-6)Online publication date: 29-Mar-2023
https://doi.org/10.1109/ESARS-ITEC57127.2023.10114838
Jayadev PBellary S(2023)IrisSeg-drunk: enhanced iris segmentation and classification of drunk individuals using Modified Circle Hough TransformIran Journal of Computer Science10.1007/s42044-023-00157-67:1(41-54)Online publication date: 28-Aug-2023
https://doi.org/10.1007/s42044-023-00157-6
Kumar AKumar ASingh MKumar PBijalwan A(2022)An Optimized Approach Using Transfer Learning to Detect Drunk DrivingScientific Programming10.1155/2022/87756072022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8775607
Chen YXue MZhang JOu RZhang QKuang P(2022) DetectDUI : An In-Car Detection System for Drink Driving and BACs IEEE/ACM Transactions on Networking10.1109/TNET.2021.312595030:2(896-910)Online publication date: Apr-2022
https://doi.org/10.1109/TNET.2021.3125950
Bhango Zvan der Haar D(2022)A Comparison of Deep Learning Methods for Inebriation Recognition in HumansImage Analysis and Processing – ICIAP 202210.1007/978-3-031-06427-2_51(610-620)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1007/978-3-031-06427-2_51
Kamath VPai SPoojary SRastogi ASrinivas K(2021)Drunkenness Face Detection using Graph Neural Networks2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)10.1109/CSITSS54238.2021.9682956(1-6)Online publication date: 16-Dec-2021
https://doi.org/10.1109/CSITSS54238.2021.9682956

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents