Eye blink detection based on motion vectors analysis

Fogelton, Andrej; Benešová, Wanda

doi:10.1016/j.cviu.2016.03.011

Cited by 48 publications

(42 citation statements)

References 22 publications

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“…Currently, the recognized changes within DDD scope are head nodding, yawning, and some varying eyelid states [27]. In order to detect these changes, behavioral methods usually go through a combination of video acquisition of the driver state and some computer vision techniques processes, [52][53] where computer vision encompasses both image processing and machine learning.…”

Section: Behavioral Methodsmentioning

confidence: 99%

Non-intrusive Driver Drowsiness Detection based on Face and Eye Tracking

Bamidele¹,

Kamardin²,

Aziz³

et al. 2019

IJACSA

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The rate of annual road accidents attributed to drowsy driving are significantly high. Due to this, researchers have proposed several methods aimed at detecting drivers' drowsiness. These methods include subjective, physiological, behavioral, vehicle-based, and hybrid methods. However, recent reports on road safety are still indicating drowsy driving as a major cause of road accidents. This is plausible because the current driver drowsiness detection (DDD) solutions are either intrusive or expensive, thus hindering their ubiquitous nature. This research serves to bridge this gap by providing a test-bed for achieving a non-intrusive and low-cost DDD solution. A behavioral DDD solution is proposed based on tracking the face and eye state of the driver. The aim is to make this research an inception to DDD pervasiveness. To achieve this, National Tsing Hua University (NTHU) Computer Vision Lab's driver drowsiness detection video dataset was utilized. Several video and image processing operations were performed on the videos so as to detect the drivers' eye state. From the eye states, three important drowsiness features were extracted: percentage of eyelid closure (PERCLOS), blink frequency (BF), and Maximum Closure Duration (MCD) of the eyes. These features were then fed as inputs into several machine learning models for drowsiness classification. Models from the K-nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and Artificial Neural Networks (ANN) machine learning algorithms were experimented. These models were evaluated by calculating their accuracy, sensitivity, specificity, miss rate, and false alarm rate values. Although these five metrics were evaluated, the focus was more on getting optimal accuracies and miss rates. The result shows that the best models were a KNN model when k = 31 and an ANN model that used an Adadelta optimizer with 3 hidden layer network of 3, 27, and 9 neurons respective. The KNN model obtained an accuracy of 72.25% with a miss rate of 16.67%, while the ANN model obtained 71.61% and 14.44% accuracy and miss rate respectively.

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Section: Behavioral Methodsmentioning

confidence: 99%

Non-intrusive Driver Drowsiness Detection based on Face and Eye Tracking

Bamidele¹,

Kamardin²,

Aziz³

et al. 2019

IJACSA

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show abstract

“…Using video recordings, eyelid movement is visible in the images and can be assessed using image processing methods. Different algorithms for that purpose are based on either the motion detection derived from differencing two consecutive images (e.g., Bhaskar, Keat, Ranganath, & Venkatesh, 2003;Chau & Betke, 2005;Fogelton & Benesova, 2016;Jiang, Tien, Huang, Zheng, & Atkins, 2013), a second-order derivative method of image differentiations (Gorodnichy, 2003), a state classification (e.g., Choi, Han, & Kim, 2011;Missimer & Betke, 2010;Pan, Sun, & Wu, 2008;Pan, Sun, Wu, & Lao, 2007), an evaluation of the color contrast or amount of visible color of specific eye regions (Cohn, Xiao, Moriyama, Ambadar, & Kanade, 2003;Danisman, Bilasco, Djeraba, & Ihaddadene, 2010;Lee, Lee, & Park, 2010), the distance between landmarks or arcs representing the upper and lower eyelid (Fuhl et al, 2016;Ito, Mita, Kozuka, Nakano, & Yamamoto, 2002;Miyakawa, Takano, & Nakamura, 2004;Moriyama et al, 2002;Sukno, Pavani, Butakoff, & Frangi, 2009), the missing regions of the open eye like the iris or pupil due to their occlusion by the upper and lower eyelid (Hansen & Pece, 2005;Pedrotti, Lei, Dzaack, & Rötting, 2011), or a combination of the described methods (Sirohey, Rosenfeld, & Duric, 2002). Instead of measuring the real distance between the upper and lower eyelid, most of these algorithms use an indirect measure (motion detection, classification, color contrast, missing eye regions) to conclude whether the eye is closed.…”

Section: Blink Detection Methodsmentioning

confidence: 99%

“…There have been different research approaches to measuring video-recorded eye blinks. Fogelton and Benesova (2016) compared different research groups, evaluating the different developed algorithms on the same data set recorded by Pan et al (2007). The numbers of labeled ground truth blinks varied by nearly 7%.…”

Section: Definition Of Eyelid Movements Categorized As Blinksmentioning

confidence: 99%

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

et al. 2017

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In this article, we examine the performance of different eye blink detection algorithms under various constraints. The goal of the present study was to evaluate the performance of an electrooculogram- and camera-based blink detection process in both manually and conditionally automated driving phases. A further comparison between alert and drowsy drivers was performed in order to evaluate the impact of drowsiness on the performance of blink detection algorithms in both driving modes. Data snippets from 14 monotonous manually driven sessions (mean 2 h 46 min) and 16 monotonous conditionally automated driven sessions (mean 2 h 45 min) were used. In addition to comparing two data-sampling frequencies for the electrooculogram measures (50 vs. 25 Hz) and four different signal-processing algorithms for the camera videos, we compared the blink detection performance of 24 reference groups. The analysis of the videos was based on very detailed definitions of eyelid closure events. The correct detection rates for the alert and manual driving phases (maximum 94%) decreased significantly in the drowsy (minus 2% or more) and conditionally automated (minus 9% or more) phases. Blinking behavior is therefore significantly impacted by drowsiness as well as by automated driving, resulting in less accurate blink detection.

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“…Then Conditional Random Field (CRF) is used to model different eye blinking stages. Fogelton and Benesova proposed a state machine to analyse eyes motion from Gunnar‐Farneback tracker for each eye. Eye motion vectors are first normalized by the intra‐ocular distance and then been used to evaluate the eye blink status.…”

Section: Related Workmentioning

confidence: 99%

Cover patches: A general feature extraction strategy for spoofing detection

Cai

Leng

et al. 2018

Concurrency and Computation

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Summary Face anti‐spoofing has attracted many attentions in security applications, such as mobile payment and entrance guard. Until now, face anti‐spoofing technique is still a challenging task. Mainstream image‐based spoofing algorithms usually use global motion or texture information to distinguish whether an input face is live or fake. However, the performance of these methods are sensitive in light changes, or images acquired from different sensors. The main reason is that spoofed face image always has slight different texture in local areas, such as landmark or salient region of face. To this end, this paper proposes a novel multi‐patches feature extraction strategy to detect spoofing. First, a set of patches with specific combination scheme is selected to cover the face image. Second, features such as hand‐crafted Gray Level Co‐occurrence Matrix (GLCM), Local Binary Patterns (LBP), or deep features are extracted from these patches. Third, all features are combined as the global descriptor of the face image, then fed into an SVM classifier to verify the anti‐spoofing detection. Experimental results show that the proposed strategy can effectively enhance the performance, concerning with the accuracy of spoofed face detection in four widely used anti‐spoofing databases.

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Eye blink detection based on motion vectors analysis

Cited by 48 publications

References 22 publications

Non-intrusive Driver Drowsiness Detection based on Face and Eye Tracking

Non-intrusive Driver Drowsiness Detection based on Face and Eye Tracking

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

Cover patches: A general feature extraction strategy for spoofing detection

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