A NOVEL DEEP LEARNING TECHNIQUE FOR ALCOHOL IMPAIRMENT USING VISUAL AND ACOUSTIC FEATURES
Journal of Medicine and Health Research,
Volume 7, Issue 2,
It has been frequently demonstrated that alcohol dependence is linked to emotional deficits, notably in the interpretation of emotional facial expressions. This paper presents the findings of several researches that investigated the impact of alcohol on speech acoustic-phonetic characteristics and on video. The method for detecting intoxication in a specific suspect using facial landmarks is the subject of the proposed study. The main objective of this research paper is to acquire an understanding of detection of alcohol of individuals before they start their job. The samples from various facial landmarks using facial video sequences and speech samples audio recordings using were then subjected to perceptual and acoustic analyses; were made of individual producing lists of sentences. This paper proposes real-time comprehensive employee alcohol impairment through our algorithm. This paper presents our views on the importance of detecting alcohol impairment considering the safety and health in the workplace at a preliminary stage with state-of-art technology before even starting a job.
It is found that facial lines changed significantly after consuming alcohol and that facial landmark vectors were the most predictive features. It is believed that consumption of alcohol produces changes in the speech production that are often described as slurred speech.
Tests revealed that under the influence of alcohol has been found to be slower, lower in all amplitudes, more prone to errors at the word, sentences and phonological levels. Our experiments are based on observations at different sites and novel deep learning architecture is proposed for giving real world performance.
- visual and acoustic feature
- neural networks
How to Cite
National Center for Statistics and Analysis. Alcoholimpaired Driving: 2014 Data. National Highway Traffic Safety Administration; Washington, DC, USA; 2015.
(Accessed on 13 December 2017)
Christoforou Z, Karlaftis MG, Yannis G. Reaction times of young alcohol-impaired drivers. Accid. Anal. Prev. 2013;61:54–62.
Steele CM, Josephs RA. Alcohol myopia: Its prized and dangerous effects. Am. Psychol. 1990;45:921–933.
Morris DH, Treloar HR, Niculete ME, McCarthy DM. Perceived danger while intoxicated uniquely contributes to driving after drinking. Alcohol. Clin. Exp. Res. 2014; 38:521–528.
Lourakis MIA. A brief description of the levenberg-marquardt algorithm implemented by levmar. Matrix.
Monahan JL, Lannutti PJ. Alcohol as social lubricant. Hum Commun Res. 2000;26(2):175–202.
Pliner P, Cappell H. Modification of affective consequences of alcohol: a comparison of social and solitary drinking. J Abnorm Psychol. 1974;83(4):418–425.
Ekman P. Expression or communication about emotion? In: Segal GE, Weisfeld CC, editors. Uniting psychology and biology: Integrative perspectives on human development. Washington: American Psychological Association.
Dimberg U. Facial electromyography and the experience of emotion. J Psychophysiol.
Zhihua X, Peng J, Ying X, Ke L. Drunk identification using far infrared imagery based on DCT features in DWT domain.
Koukiou G, Anastassopoulos V. Neural networks for identifying drunk persons using thermal infrared imagery.
Hiass RS, Arandjelovi´c O, Bendada H, Maldague X. Vesselness features and the inverse compositional AAM for robust face recognition sing thermal IR.
Bhuyan MK, Dhawle S, Sasmal P, Koukiou G. Intoxicated person identification using thermal infrared images and gait.
Koukiou G, Anastassopoulos V. Drunk person identification using thermal infrared images.
Koukiou G, Anastassopoulos V. Facial blood vessels activity in drunk persons using thermal infrared. In Proceedings of the 4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011), London, UK; 3–4 November 2011.
Koukiou G, Anastassopoulos V. Drunk person screening using eye thermal signatures. J. Forensic Sci. 2016;61:259–264.
Florian Eyben, Martin Wöllmer, Björn Schuller. Opensmile: The munich versatile and fast open-source audio feature extractor. In Proceedings of the 18th ACM International Conference on Multimedia; 2010.
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