A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification

Bakheet, Samy; Al-Hamadi, Ayoub

doi:10.3390/brainsci11020240

Cited by 62 publications

(36 citation statements)

References 36 publications

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“…al [31], and Bakheet et. al [32] respectively. The sensitivity value of the proposed approach is reaching 100% compared with 97.13%, 94.74%, and 93.5% for algorithms by Biswal et.…”

Section: E) Comparisonsmentioning

confidence: 96%

“…al [31], and Bakheet et. al [32] in addition to the proposed approaches. The comparison is divided into two parts, the video streaming-based algorithms and physiological signals (EEG) based algorithms.…”

Section: E) Comparisonsmentioning

confidence: 99%

“…al [31], and Bakheet et. al [32] have been compared with the proposed deep learning approach. For the EEG signal's part, algorithms Ko et.…”

Section: E) Comparisonsmentioning

confidence: 99%

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Fatigue State Detection for Tired Persons in Presence of Driving Periods

et al. 2022

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“…al [31], and Bakheet et. al [32] respectively. The sensitivity value of the proposed approach is reaching 100% compared with 97.13%, 94.74%, and 93.5% for algorithms by Biswal et.…”

Section: E) Comparisonsmentioning

confidence: 96%

Section: E) Comparisonsmentioning

confidence: 99%

See 1 more Smart Citation

Fatigue State Detection for Tired Persons in Presence of Driving Periods

et al. 2022

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“…The key idea of the SURF detection algorithm is to detect points of interest (i.e., keypoints) such as corners or blob-like structures from an image in the places where the determinant of the Hessian matrix has a maximum value [26]. While the detector locates the keypoints, the descriptor describes the features of these keypoints and constructs the feature vectors of the detected keypoints [27].…”

Section: Speeded Up Robust Feature (Surf) Algorithmmentioning

confidence: 99%

A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms

2022

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Amongst all biometric-based personal authentication systems, a fingerprint that gives each person a unique identity is the most commonly used parameter for personal identification. In this paper, we present an automatic fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques. Initially, a variant of adaptive histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast of fingerprint images. The fingerprint is then authenticated by picking a small amount of information from some local interest points called minutiae point features. These features are extracted from the thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresponding Harris-SURF feature vectors of two feature points is used as a feature matching similarity measure of two fingerprint images. Moreover, an iterative algorithm called RANSAC (RANdom SAmple Consensus) is applied for fine matching and to automatically eliminate false matches and incorrect match points. Quantitative experimental results achieved on FVC2002 DB1 and FVC2000 DB1 public domain fingerprint databases demonstrate the good performance and feasibility of the proposed framework in terms of achieving average recognition rates of 95% and 92.5% for FVC2002 DB1 and FVC2000 DB1 databases, respectively.

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“…This method calculates the local energy in the video based on the Magno feature of the given video to determine whether the eyes are blinking and the mouth is open. In the traditional machine visual methods, Bakheet et al [13] improved the HOG feature and used the naive Bayes method to classify the eye state and achieved 85.62% detection accuracy in the NTHU-DDD dataset. Akrout et al [14] used the optical flow method to detect whether the mouth is open and used Haar wavelets and circular Hough transform to detect the eyes' opening angle.…”

Section: Related Workmentioning

confidence: 99%

Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation

Zhang

Cao

et al. 2021

Applied Sciences

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Driver fatigue is the culprit of most traffic accidents. Visual technology can intuitively judge whether the driver is in the state of fatigue. A driver fatigue detection system based on the residual channel attention network (RCAN) and head pose estimation is proposed. In the proposed system, Retinaface is employed for face location and outputs five face landmarks. Then the RCAN is proposed to classify the state of eyes and the mouth. The RCAN includes a channel attention module, which can adaptively extract key feature vectors from the feature map, which significantly improves the classification accuracy of the RCAN. In the self-built dataset, the classification accuracy of the eye state of the RCAN reaches 98.962% and that of the mouth state reaches 98.561%, exceeding other classical convolutional neural networks. The percentage of eyelid closure over the pupil over time (PERCLOS) and the mouth opening degree (POM) are used for fatigue detection based on the state of eyes and the mouth. In addition, this article proposes to use a Perspective-n-Point (PnP) method to estimate the head pose as an essential supplement for driving fatigue detection and proposes over-angle to evaluate whether the head pose is excessively deflected. On the whole, the proposed driver fatigue system integrates 3D head pose estimation and fatigue detection based on deep learning. This system is evaluated by the four datasets and shows success of the proposed method with their high performance.

show abstract

A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification

Cited by 62 publications

References 36 publications

Fatigue State Detection for Tired Persons in Presence of Driving Periods

Fatigue State Detection for Tired Persons in Presence of Driving Periods

A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms

Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation

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