A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor

Kim, Ki Wan; Hong, Hyung Gil; Nam, Gi Pyo; Park, Kang Ryoung

doi:10.3390/s17071534

Cited by 72 publications

(29 citation statements)

References 37 publications

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“…In the last decade, CNN has been proved as the most powerful tool for image-related tasks using deep learning. CNN delivers good performances in computer vision applications such as human detection [ 48 ], open and close eye detection [ 49 ], gender recognition [ 50 ], pedestrian detection [ 51 ], banknote classification [ 52 ], appearance-based gaze estimation [ 53 ], and object detection using faster R-CNN [ 54 ]; more CNN applications can be found in [ 55 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

Arsalan

Naqvi

Kim

et al. 2018

Sensors

Self Cite

100

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The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.

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Section: Proposed Methodsmentioning

confidence: 99%

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

Arsalan

Naqvi

Kim

et al. 2018

Sensors

Self Cite

100

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“…Feature extraction is one type of dimensionality reduction where useful parts of an image represented as a feature vector. In this paper features from the eye region images are extracted using a Convolutional Neural Network (CNN) [22][23][24].…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

Deep CNN: A Machine Learning Approach for Driver Drowsiness Detection Based on Eye State

Chirra¹,

Reddy²,

Kolli³

2019

RIA

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Driver drowsiness is one of the reasons for large number of road accidents these days. With the advancement in Computer Vision technologies, smart/intelligent cameras are developed to identify drowsiness in drivers, thereby alerting drivers which in turn reduce accidents when they are in fatigue. In this work, a new framework is proposed using deep learning to detect driver drowsiness based on Eye state while driving the vehicle. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm is used in this work. Stacked deep convolution neural network is developed to extract features from dynamically identified key frames from camera sequences and used for learning phase. A SoftMax layer in CNN classifier is used to classify the driver as sleep or non-sleep. This system alerts driver with an alarm when the driver is in sleepy mood. The proposed work is evaluated on a collected dataset and shows better accuracy with 96.42% when compared with traditional CNN. The limitation of traditional CNN such as pose accuracy in regression is overcome with the proposed Staked Deep CNN.

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“…They used a camera to capture the tester’s facial image and built a gaze-tracking model whereby the tester could control the iPad’s applications by moving their eyes or head. Kim et al [ 14 ] built an image acquisition system that is used to distinguish between open and closed human eyes, and they trained a high-precision eye detection model based on CNNs. Krafka et al [ 15 ] used mobile phones to capture the face images of different testers and located the human eye region and gaze by CNNs.…”

Section: Related Workmentioning

confidence: 99%

Etracker: A Mobile Gaze-Tracking System with Near-Eye Display Based on a Combined Gaze-Tracking Algorithm

Hong

Wen

et al. 2018

Sensors

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Eye tracking technology has become increasingly important for psychological analysis, medical diagnosis, driver assistance systems, and many other applications. Various gaze-tracking models have been established by previous researchers. However, there is currently no near-eye display system with accurate gaze-tracking performance and a convenient user experience. In this paper, we constructed a complete prototype of the mobile gaze-tracking system ‘Etracker’ with a near-eye viewing device for human gaze tracking. We proposed a combined gaze-tracking algorithm. In this algorithm, the convolutional neural network is used to remove blinking images and predict coarse gaze position, and then a geometric model is defined for accurate human gaze tracking. Moreover, we proposed using the mean value of gazes to resolve pupil center changes caused by nystagmus in calibration algorithms, so that an individual user only needs to calibrate it the first time, which makes our system more convenient. The experiments on gaze data from 26 participants show that the eye center detection accuracy is 98% and Etracker can provide an average gaze accuracy of 0.53° at a rate of 30–60 Hz.

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A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor

Cited by 72 publications

References 37 publications

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

Deep CNN: A Machine Learning Approach for Driver Drowsiness Detection Based on Eye State

Etracker: A Mobile Gaze-Tracking System with Near-Eye Display Based on a Combined Gaze-Tracking Algorithm

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