Non-intrusive car driver's emotion recognition using thermal camera

Kolli, Abhiram; Fasih, Alireza; Machot, Fadi Al; Kyamakya, Kyandoghere

doi:10.1109/inds.2011.6024802

Cited by 41 publications

(30 citation statements)

References 30 publications

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“…Measuring the heat distribution in the face can give information about the anxiety level [141], the emotion of car drivers [95], or it can be used for automatic blush detection [66]. For such systems to work automatically, it is important that the system is able to follow the tissue of interest over time.…”

Section: Facial Analysismentioning

confidence: 99%

Thermal cameras and applications: a survey

Gade

Moeslund

2013

Machine Vision and Applications

608

269

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Thermal cameras are passive sensors that capture the infrared radiation emitted by all objects with a temperature above absolute zero. This type of camera was originally developed as a surveillance and night vision tool for the military, but recently the price has dropped, significantly opening up a broader field of applications. Deploying this type of sensor in vision systems eliminates the illumination problems of normal greyscale and RGB cameras.This survey provides an overview of the current applications of thermal cameras. Applications include animals, agriculture, buildings, gas detection, industrial, and military applications, as well as detection, tracking, and recognition of humans. Moreover, this survey describes the nature of thermal radiation and the technology of thermal cameras.

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Section: Facial Analysismentioning

confidence: 99%

Thermal cameras and applications: a survey

Gade

Moeslund

2013

Machine Vision and Applications

608

269

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“…Visual cues of drivers can be used for finding emotional state, fatigue, or abnormal behavior. Visual behavior based methods can be classified into multi cameras-based [25][26][27][28][29] and single camera-based methods [30][31][32][33][34][35][36][37]. Multi cameras-based methods were also been used by researchers for classifying driver's behaviors while using visual cues.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Drivers Emotion Classification System in Time Series Data for Remote Applications

et al. 2020

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Aggressive driving emotions is indeed one of the major causes for traffic accidents throughout the world. Real-time classification in time series data of abnormal and normal driving is a keystone to avoiding road accidents. Existing work on driving behaviors in time series data have some limitations and discomforts for the users that need to be addressed. We proposed a multimodal based method to remotely detect driver aggressiveness in order to deal these issues. The proposed method is based on change in gaze and facial emotions of drivers while driving using near-infrared (NIR) camera sensors and an illuminator installed in vehicle. Driver's aggressive and normal time series data are collected while playing car racing and truck driving computer games, respectively, while using driving game simulator. Dlib program is used to obtain driver's image data to extract face, left and right eye images for finding change in gaze based on convolutional neural network (CNN). Similarly, facial emotions that are based on CNN are also obtained through lips, left and right eye images extracted from Dlib program. Finally, the score level fusion is applied to scores that were obtained from change in gaze and facial emotions to classify aggressive and normal driving. The proposed method accuracy is measured through experiments while using a self-constructed large-scale testing database that shows the classification accuracy of the driver's change in gaze and facial emotions for aggressive and normal driving is high, and the performance is superior to that of previous methods.Association Foundation for Traffic safety, published in 2009, that the aggressive behavior of driver causes 56% of traffic accidents [2]. Besides precious human lives, people, company, and government also lose billions of dollars due to road accidents. For this reason, aggressive driving behavior must be strongly discouraged that will result in reduction of the number of traffic accidents.The classification of aggressive and normal behavior is an important issue that can be used to increase awareness of driving habits of drivers as many drivers are over confident and are unaware of their bad driving habits [3]. If we can automatically identify the drivers driving behaviors, the drivers can be aware of their bad habits and assist them to avoid potential car accidents. Other than this if, monitoring results could be sent back to a security observing server of the local police station that could help to automatically detect aggressive drivers. The conventional method to keep a check on aggressive driving is by police patrolling, but, due to lack of police force, all roads cannot be simultaneously monitored and it also costs a lot [4]. The need of intelligent surveillance system is increasing with the increase in population. The advance driver assistance system (ADAS) that can monitor driver's attention and driving behavior can improve road safety, which will also enhance the effectiveness of the ADAS [5]. Many challenges are faced by these real time systems that ...

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“…Therefore, e-healthcare systems are highly required to improve the overall quality of patients' life. Currently, there are several works to detect emotions using different types of sensors, e.g., microphones to analyse the audio signals [3] or thermal cameras [9]. Additionally, e-healthcare systems based on biological signals started to get the attention from the research community.…”

Section: Introductionmentioning

confidence: 99%

A novel EEG-based emotion recognition approach for e-healthcare applications

Ali

Machot

Mosa

et al. 2016

Proceedings of the 31st Annual ACM Symposium on Applied Computing

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Electroencephalogram (EEG) signals play an important role in e-healthcare systems, especially to recognise the mental state of patients that could need a special care. This paper presents an EEG-based emotion recognition approach to detect the emotional state of patients for Ambient Assisted Living (AAL). The proposed approach combines wavelet energy, modified energy and wavelet entropy features to classify four different classes of emotions. Three different classifiers are used (quadratic discriminant analysis (QDA), knearest neighbor, and support vector machines) to recognise the emotion of patients robustly. The new approach is tested using the EEG database "DEAP" using four electrodes. It shows high performance compared to existing algorithms. An overall classification accuracy of 83.2% is obtained.

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Non-intrusive car driver's emotion recognition using thermal camera

Cited by 41 publications

References 30 publications

Thermal cameras and applications: a survey

Thermal cameras and applications: a survey

Deep Learning-Based Drivers Emotion Classification System in Time Series Data for Remote Applications

A novel EEG-based emotion recognition approach for e-healthcare applications

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