An Eye State Recognition Method for Drowsiness Detection

Wu, Yu-Liang; Lee, Ting-Wei; Wu, Quen-Zong; Liu, Heng-Sung

doi:10.1109/vetecs.2010.5493951

Cited by 25 publications

(13 citation statements)

References 11 publications

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“…C. Xu et al adopted the LBP approach [12] for representing eye images and then construct an adaboosting classifier for eye state recognition [13]. Similarly, Y.-S. Wu et al also employed the LBP approach but the SVM classification was adopted [14]. These two works presented the good performance of using the LBP feature for eye image representation.…”

Section: Related Workmentioning

confidence: 99%

An Efficient and Effective Framework for Eye Region Detection and Eye State Recognition

Chiang

2014

AIVP

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This paper proposes a framework to treat the eye information in a face image including the eye region detection and the eye state recognition. In order to make it possible to employ our approach to a real application in practice, our goal in this paper is to design not only a fast enough but also a high performance framework for the eye region detection and the eye state recognition. Our proposed framework mainly contains two parts: the first is to locate the eye regions in a face image, and the second is to recognize the states, either open or closed, of the eye regions. When a frame is captured from a video sequence, a face detection method is first performed to determine the positions of face regions. Next, face regions are converted into binary images and then we perform the horizontal and the vertical projections to locate the eye regions. Two visual features containing the intensity values of pixels and local binary pattern (LBP) are extracted from eye regions to classify the eye states with the support vector machine (SVM) approach. This paper also demonstrates a several experiments to present the efficiency and effectiveness of our proposed framework.

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Section: Related Workmentioning

confidence: 99%

An Efficient and Effective Framework for Eye Region Detection and Eye State Recognition

Chiang

2014

AIVP

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“…The second step is state recognition including eye and mouth states (open or close). Wu et al [14] used local binary patterns (LBPs) to find the region of the left eye and detected the eye state by SVM; Song et al [15] proposed a new feature descriptor named multi‐scale histograms of principal oriented gradients (MultiHPOG) to improve the robustness against image noise and scale changes, besides they collected their own dataset (CEW dataset) to make experiments; Dong compared different methods of eye state estimation such as random forest, random ferns and SVM [16].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical CNN‐based real‐time fatigue detection system by visual‐based technologies using MSP model

Zhu

Chen

et al. 2018

IET Image Processing

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“…Wu et al [5] proposed a method of recognizing the state of the eye. They use the Haar-like feature and the Adaboost classifier [6] to find the face area.…”

Section: Introductionmentioning

confidence: 99%

Design Simulation and Performance Analysis of Real Time Facial Features Monitoring for Drowsiness Detection Using Support Vector Machine

Mundra¹,

Todwal²

2018

International Journal of Innovative Research in Electrical, Ele

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This article describes an effective method for performing drowsiness testing through three well-defined stages. These three stages are using Viola Jones for facial characterization, eye tracking and yawn detection. Once the face is detected, the system illumination is left unchanged by separately segmenting the skin portion and considering only the color components to reject most of the non-face image background based on the skin color. Eye tracking and yawn detection are accomplished by correlation coefficient template matching. Feature vectors from each of the above stages are connected, and a binary linear support vector machine classifier is used to classify successive frames into fatigue and non-fatigue states, and alerts the former if it is above a threshold time. Extensive real-time experiments have proven that the proposed method is very effective in finding sleepiness and warning

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An Eye State Recognition Method for Drowsiness Detection

Cited by 25 publications

References 11 publications

An Efficient and Effective Framework for Eye Region Detection and Eye State Recognition

An Efficient and Effective Framework for Eye Region Detection and Eye State Recognition

Hierarchical CNN‐based real‐time fatigue detection system by visual‐based technologies using MSP model

Design Simulation and Performance Analysis of Real Time Facial Features Monitoring for Drowsiness Detection Using Support Vector Machine

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