Tracking a Driver’s Face against Extreme Head Poses and Inference of Drowsiness Using a Hidden Markov Model
AbstractThis study presents a new method to track driver’s facial states, such as head pose and eye-blinking in the real-time basis. Since a driver in the natural driving condition moves his head in diverse ways and his face is often occluded by his hand or the wheel, it should be a great challenge for the standard face models. Among many, Active Appearance Model (AAM), and Active Shape Model (ASM) are two favored face models. We have extended Discriminative Bayesian ASM by incorporating the extreme pose cases, called it Pose Extended—Active Shape model (PE-ASM). Two face databases (DB) are used for the comparison purpose: one is the Boston University face DB and the other is our custom-made driving DB. Our evaluation indicates that PE-ASM outperforms ASM and AAM in terms of the face fitting against extreme poses. Using this model, we can estimate the driver’s head pose, as well as eye-blinking, by adding respective processes. Two HMMs are trained to model temporal behaviors of these two facial features, and consequently the system can make inference by enumerating these HMM states whether the driver is drowsy or not. Result suggests that it can be used as a driver drowsiness detector in the commercial car where the visual conditions are very diverse and often tough to deal with. View Full-Text
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Choi, I.-H.; Jeong, C.-H.; Kim, Y.-G. Tracking a Driver’s Face against Extreme Head Poses and Inference of Drowsiness Using a Hidden Markov Model. Appl. Sci. 2016, 6, 137.
Choi I-H, Jeong C-H, Kim Y-G. Tracking a Driver’s Face against Extreme Head Poses and Inference of Drowsiness Using a Hidden Markov Model. Applied Sciences. 2016; 6(5):137.Chicago/Turabian Style
Choi, In-Ho; Jeong, Chan-Hee; Kim, Yong-Guk. 2016. "Tracking a Driver’s Face against Extreme Head Poses and Inference of Drowsiness Using a Hidden Markov Model." Appl. Sci. 6, no. 5: 137.
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