Real Time Eye Detector with Cascaded Convolutional Neural Networks

Li, Bin; Hong, Fei

doi:10.1155/2018/1439312

Cited by 32 publications

(10 citation statements)

References 19 publications

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“…Using only CNNs for accurate gaze tracking requires a large amount of training data and a long training time. Therefore, when performing the initial calibration step, we collected multiple eye images and used our previous work [ 41 ] to detect eye centers

. Then, we counted the mean value

of these eye center positions, where i represents the

calibration mark, and j denotes the

eye image.…”

Section: The Proposed Methodsmentioning

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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. Then, we counted the mean value

of these eye center positions, where i represents the

calibration mark, and j denotes the

eye image.…”

Section: The Proposed Methodsmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…It has attracted great interests nowadays. Bin Li presented an effective cascaded CNNs methods to detect the eye location in facial images, the first CNN can classify the region as left or right eye, the second is for detection [49]. Harini K employed ensemble learning with the ResNet10 model to track eye for iphone [50].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Iris Tracking Using Deep Regression Networks for Robotic Ophthalmic Surgery

Qiu

Yang

et al. 2020

IEEE Access

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Robotic-assisted platforms are expected to guarantee the accuracy of surgical operation and accelerate its learning curve. Iris tracking can guide the robotic manipulator during the operation. However, few researches focused on it during surgery. It is a big challenge due to the deformation of the iris and occlusion caused by instruments. A novel real-time iris tracking method based on a regression network are proposed to meet the speed and accuracy requirements of the ophthalmic robotic system. It utilizes the low-level visual features and high-level semantic meanings from different layers to capture the discriminative representation of the iris target. Then the bottleneck layers are added to improve computation efficiency. Furthermore, a multi-loss function is designed by jointly learning Absolute loss and Euclidean loss. Finally, the experimental results under the typical surgical scene demonstrate that iris tracker achieves an accuracy of 89.16% and a real-time speed of 134fps with GPU, which is suitable for the ophthalmic robotic system to perform real-time robotic manipulation. INDEX TERMS Robotic surgery, deep learning, cataract surgery, iris tracking, real-time tracking.

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“…Another algorithm that was used is the Cascaded CNN which was used to detect eye patterns of the driver (Fu et al, 2016;Li & Fu, 2018). The way it works was that from the image of the face it detects the eye region of the driver to monitor the movement of eye.…”

Section: Yolo Algorithmmentioning

confidence: 99%

“…Studies on prevention of accidents caused due to drowsiness were studied using an eye blink sensor (Li & Fu, 2018) in automobiles and validated using Proteus software and programming language C ++ (Eduku, 2016). In the present study, the core of the APB was a decision tree that inferred on the action to be taken, based on the inputs from the various sensors and as per the flow mentioned in Figure 01.…”

Section: What the Proposed System Doesmentioning

confidence: 99%