Detection of the gaze direction using the time-varying image processing

Norimatsu, Y.; Mita, S.; Kozuka, Kazuhiro; Nakano, Tomoaki; Yamamoto, Shin

doi:10.1109/itsc.2003.1251924

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…A driver fatigue detection algorithm has been proposed based on the eye tracking and dynamic template matching [15]. The detection of the gaze direction using the time-varying image processing has been studied in [16] where the facial direction and the gaze direction were detected separately, and then they were integrated into a final gaze direction. Taking the openness of mouth and eye, respectively, and the vertical distance between eyebrows and eyes as inputs, a fuzzy neural network model was constructed for detecting fatigue [17].…”

Section: Physiological Features Onlymentioning

confidence: 99%

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Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

Yang

Lin

Bhattacharya

2007

EURASIP J. Adv. Signal Process.

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To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i) casual or contextual feature, (ii) contact feature, (iii) contactless feature, and (iv) performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK) model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA), is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue). We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.

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Section: Physiological Features Onlymentioning

confidence: 99%

“…A number of techniques [28,50,[53][54][55] are available to determine the weight vector w of (16). In this study, we take a combined technique from the literature [53,55].…”

Section: Features Availablementioning

confidence: 99%

Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

Yang

Lin

Bhattacharya

2007

EURASIP J. Adv. Signal Process.

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“…All of the above processes in the driver monitor system are improvements on techniques used in the vehicle-installed system previously developed [13][14][15]. …”

Section: Detection Process In Driver Monitor Systemmentioning

confidence: 99%

“…The driver monitor system used in this simulator includes improved versions of the imaging system and the techniques for detection of gaze direction and blink which were developed earlier by the authors [12][13][14][15]. The information display system consists of a navigation screen display and a warning presentation system for the driving assistance system.…”

Section: Introductionmentioning

confidence: 99%

A driving simulator with driver monitor system

Adachi¹,

Kobayashi²,

Itoh³

et al. 2007

Systems & Computers in Japan

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SUMMARYThe driving simulators hitherto developed were intended for investigation of active safety techniques, verification of vehicle movement models, etc., without using an actual vehicle. But there subsequently arose a need for a system that could be used to investigate ways of providing driving and safety assistance to drivers in the aging society of the future. There has been no driving simulator available for such evaluations. The authors have developed a driving simulator for evaluation of driver-assistance systems with the following installations. A driver monitor camera is installed in the inner mirror unit of the car, and the level of consciousness of the driver is estimated by video processing. A technique for measuring the gaze direction is installed. Techniques for character size transformation in the display and warnings are investigated with reference to the lowered visual acuity of elderly drivers. A verification experiment is performed on braking distance, which is affected by the increased reaction time when the level of consciousness is low. The effectiveness of the system as verified by experiment is reported.

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A Driver Fatigue Recognition Model Using Fusion of Multiple Features

Yang

Lin

Bhattacharya

2005 IEEE International Conference on Systems, Man and Cybernetics

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Detection of the gaze direction using the time-varying image processing

Cited by 4 publications

References 3 publications

Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

A driving simulator with driver monitor system

A Driver Fatigue Recognition Model Using Fusion of Multiple Features

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