Using the Active Appearance Model to detect driver fatigue

Saeed, Ifrah; Wang, A.; Senaratne, Rajinda; Halgamuge, Saman K.

doi:10.1109/iciafs.2007.4544792

Cited by 14 publications

(12 citation statements)

References 15 publications

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“…This paper improves the model-land marks using in driver fatigue detection with AAM that originally been proposed by Isaam Saeed et al [8], and reduces redundant facial texture in formation to accelerate the fitting speed. We also propose to convert the eye contour aspect ratio into PERCLOS to detect driver fat igue.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 2. Landmarks contain half face [8] Tsuyoshi Moriyama and Takeo Kanade proposes a method of automated individualization of eye region model, which div ides an eye region into three subregions that include the upper eyelid subregion, the lower eyelid subregion, and the palpebral fissure subregion (the aperture between the eyelids)(See Figure 3)[7], wh ich is useful for our driver fatigue detection system. Figure 3.…”

Section: Trai Ning Of An Aam Is Accomplishedmentioning

confidence: 99%

“…It provides a less intrusive experience for the driver, as opposed to EEG methods, which are mo re accurate, but are considered intrusive. Isaam Saeed et al present a video-based driver fatigue detection system, wh ich uses the Active Appearance Model as a means to track rig id and non-rigid motion of a face in an input video sequence and propose an Active Appearance Model specific to the ocular region of the face to place greater focus on the eyes, as fatigue cues based on ocular features [8]. However because Isaam Saeed et al directly use AAM on face, their model contains too many facial textures which are useless for eyes and reduces the fitting speed and detection rate.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-time driver fatigue detection based on simplified landmarks of AAM

Dong

Xie

2010

The 2010 International Conference on Apperceiving Computing and Intelligence Analysis Proceeding

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Using Active Appearance Model to detect driver fatigue has been proposed in past research. This paper proposes a rapid method of driver fatigue detection by applying Active Appearance Model with simplified landmarks only around eyes, and also proposes to convert the eye contour aspect ratio into PERCLOS to judge the degree of the driver fatigue. Compared to the original method, this method reduces redundant facial texture information, and accelerates the speed of eye state detection while improving detection accuracy rate of driver fatigue. The experiments results show that this method is fast and effective that can be used in real-time detection.

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Section: Discussionmentioning

confidence: 99%

Section: Trai Ning Of An Aam Is Accomplishedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-time driver fatigue detection based on simplified landmarks of AAM

Dong

Xie

2010

The 2010 International Conference on Apperceiving Computing and Intelligence Analysis Proceeding

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“…The head position sensor system micro‐cod detection system) is conceptually designed to detect head nodding angle in real time by assessing the space coordinates of driver head through conductivity measurements [21]. The vertical translation mode of the active appearance mode is used to reflect head motion, and the cyclic vertical head motion is considered to be the head‐nodding [22]. Ji et al ., [23] use the wide angle camera focusing on the face to track the driver's head movement.…”

Section: Introductionmentioning

confidence: 99%

Driver fatigue evaluation model with integration of multi‐indicators based on dynamic Bayesian network

Wei

Fan

et al. 2015

IET Intelligent Transport Systems

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Electroencephalogram (EEG) data are an effective indicator to evaluate driver fatigue, but it is usually disturbed by noise. The frequent head nodding, as well as the time of day and total driving time, also have very close relationship with driver fatigue. All these factors should be taken into account for comprehensive driver fatigue evaluation. 50 drivers are recruited to take part in the fatigue-oriented experiment on the driving simulator. Based on the EEG samples, the EEGbased indicator of driver fatigue has been established by artificial neural network. Subsequently, a new algorithm is present to compute the head nodding angle with posture data from the passive tools fixed on the driver's head and trunk, respectively, and then head-based indicator of driver fatigue is determined. Finally, a new evaluation model of driver fatigue is established with integration of four fatigue-based indicators with DBN (Dynamic Bayesian Network). The results show that it is more accurate to evaluate the driver fatigue compared with the sole EEG-based indicator.

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“…The later is to extract features from components of a face, such as nose, mouth, eyes and eyebrows. For instance, in [5] only the eye shape changes are extracted.…”

Section: Introductionmentioning

confidence: 99%

Smile recognition based on the fusion of the face texture feature and the mouth shape feature

Li¹

2016

Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

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Abstract. Smile recognition has recently attracted significant attention due to the increasing accessibility in some digital products. Traditionally, the mouth feature has been extracted for smile recognition. However, it only extracts the features in the mouth region, ignoring the fact that face feature contribute to characterize expressions. In this paper, in order to overcome the drawbacks and make full use of the merits of both features, we propose a novel feature fusion approach for the smile recognition. In our approach, the mouth features are described by HOG descriptor and the face features are represented by Local Binary Pattern (LBP) histograms. Then, the two features are fused by discriminative canonical correlation analysis (DCCA). Robust Support Vector Machine (SVM) classifier is used for classification in our experiment and satisfactory results of experiments based on two databases are obtained finally.

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Using the Active Appearance Model to detect driver fatigue

Cited by 14 publications

References 15 publications

Real-time driver fatigue detection based on simplified landmarks of AAM

Real-time driver fatigue detection based on simplified landmarks of AAM

Driver fatigue evaluation model with integration of multi‐indicators based on dynamic Bayesian network

Smile recognition based on the fusion of the face texture feature and the mouth shape feature

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