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For this study, using Bayesian Network (BN) to graphically express the interrelationship between safety confirmation behaviors for driving scene and driver's internal state, we analyze correlations between characteristic body information (i.e., eye-gaze / face orientation) and operation information (i.e., steering wheel, accelerator, and brake), when switching from "concentrating state"...
For this study, we defined a "concentration state" when a driver performs only driving tasks, and a "distraction state" when a driver performs a driving task and a mental arithmetic task simultaneously. From results of these driving tests, we elucidate the characteristics of safety confirmation behaviors by near-misses according to differences between two driving conditions when...
This paper presents a gender-specific stress model to analyze the psychological stress factors on intentional facial expressions. We have focused on the relationship between facial expression intensity and Stress Response Scale (SRS-18). In this paper, we extract three facial expressions (i.e., happiness, anger, and sadness) from the basic six facial expressions defined by Ekman, and then represent...
This paper presents a method to create an individual model to describe relations between facial expressions and stress patterns using Bayesisan networks. We extracted relations between three facial expressions (happiness, anger, and sadness) for the basic six facial expressions defined by Ekman and psychological stress factors (dysphoria and anxiety, displeasure and anger, and lassitude).
This paper presents an unsupervised clustering method to classify the optimal number of clusters from a given dataset based solely on the image characteristics. The proposed method contains a feature based on the hybridization of two unsupervised neural networks, Self-Organizing Maps (SOMs) and Fuzzy Adaptive Resonance Theory (ART), which has a seamless mapping procedure comprising the following two...
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