Face Pose Estimation From Video Sequence Using Dynamic Bayesian Network

Suandi, Shahrel Azmin; Enokida, Shuichi; Ejima, Toshiaki

doi:10.1109/wmvc.2008.4544053

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Previous work done by Suandi et al [4] to estimate face pose was done based on property-based approach, where two important cues defined as horizontal ratio and vertical ratio are used as the properties (evidences) to infer the pose using Dynamic Bayesian Network (DBN). To compute the evidence for DBN inference, the method utilizes merely three of the important facial features, i.e., pupils, mouth center and skin region.…”

Section: A) Methods Based On Bioelectric and Nervous Signals B) Methomentioning

confidence: 99%

AU measurements from cascaded Adaboost for driver drowsiness detection

Loh¹,

Suandi

2013

2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA)

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Driver fatigue has getting more attentions from researchers. One of the methods to recognize driver fatigue is by monitoring driver's facial expression. Facial Action Coding System (FACS) is well-known in facial behavior sciences and Action Unit (AU) is used as the measurement. In this work, AU coded facial image is represented by Gabor filters in different orientations and frequencies, which are classified into respective classes using cascaded AdaBoost. The result is validated with ground truth information given in the database. Results show the validation of AU measurement in computer vision method and will be further applied in driver monitoring system to predict the driver's fatigue level.

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Section: A) Methods Based On Bioelectric and Nervous Signals B) Methomentioning

confidence: 99%

AU measurements from cascaded Adaboost for driver drowsiness detection

Loh¹,

Suandi

2013

2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA)

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“…It includes Kernel Machine based learning method [6], RVM method [8], SVM method [1], Dynamic Bayesian Network method [9] and etc.…”

Section: Introductionmentioning

confidence: 99%

Face pose estimation based on EHMM and SVM

Kim¹,

2011

2011 International Conference on Computer Science and Service System (CSSS)

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Estimating pose of human face plays important role in lip reading. Mouth shape varies on face pose variation. Therefore, pose estimation will be front end of lip reading system. Embedded HMM can be successfully applied in estimating pose of human face. Advantage of EHMM based pose estimation method is non-sensitive in parallel shift and scale varying. On the other side, SVM is the classifier which has strong discrimination ability. This paper suggests EHMM and SVM based human face pose estimation method. It was experimented on CubiC FacePix database and was estimated with several range of angle. Experiment proved validity of this algorithm.

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Neuron-Wise Inhibition Zones and Auditory Experiments

Weng

2019

IEEE Trans. Ind. Electron.

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Face Pose Estimation From Video Sequence Using Dynamic Bayesian Network

Abstract: Abstract

Cited by 3 publications

References 14 publications

AU measurements from cascaded Adaboost for driver drowsiness detection

AU measurements from cascaded Adaboost for driver drowsiness detection

Face pose estimation based on EHMM and SVM

Neuron-Wise Inhibition Zones and Auditory Experiments

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