RE-PUPIL: resource efficient pupil detection system using the technique of average black pixel density

Navaneethan, S.; Nandhagopal, N.

doi:10.1007/s12046-021-01644-x

Cited by 25 publications

(3 citation statements)

References 23 publications

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“…The prototype system has been described as being used to locate the iris in visible wavelength (VW) images, but it can also be used in near-infrared (NIR) images. Navaneethan et al [13] proposed using average black pixel density to detect the pupil region on an FPGA. Simple threshold and morphology techniques were used in this work, which is unsuitable for larger datasets.…”

Section: Related Workmentioning

confidence: 99%

The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

Navaneethan¹,

Sreedhar²,

Padmakala³

et al. 2023

Computer Systems Science and Engineering

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The pupil recognition method is helpful in many real-time systems, including ophthalmology testing devices, wheelchair assistance, and so on. The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size, occlusion of eyelids, and eyelashes. Deep Convolutional Neural Networks (DCNN) are being used in pupil recognition systems and have shown promising results in terms of accuracy. To improve accuracy and cope with larger datasets, this research work proposes BOC (BAT Optimized CNN)-IrisNet, which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil region. The proposed method is based on very deep architecture and many tricks from recently developed popular CNNs. Experiment results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight, easy to implement, and of cutting-edge accuracy. Finally, the region-based black box method for determining pupil center coordinates was introduced. The proposed architecture was tested using various IRIS databases, including the CASIA (Chinese academy of the scientific research institute of automation) Iris V4 dataset, which has 99.5% sensitivity and 99.75% accuracy, and the IIT (Indian Institute of Technology) Delhi dataset, which has 99.35% specificity and MMU (Multimedia University) 99.45% accuracy, which is higher than the existing architectures.

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Section: Related Workmentioning

confidence: 99%

The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

Navaneethan¹,

Sreedhar²,

Padmakala³

et al. 2023

Computer Systems Science and Engineering

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“…Another function that our ETS using FPGA can provide is detecting the size of pupil which is crucial in some applications such as medical diagnosis because diameter of the pupil varies in reaction to illumination. The most commonly used approach of detecting pupil size is finding the largest contour in the image with assumption that it is the pupil and fitting an ellipse or a circle to that [9], [12][13][14].…”

Section: Pupil Size Detectionmentioning

confidence: 99%

“…However, several FPGA-based works fail to achieve high accuracy due to not mitigating this error properly [8]. Also, various works deal with that using software-hardware collaborative design which wastes benefits of FPGA hardware acceleration and is not appropriate for real-time applications [9].…”

Section: Introductionmentioning

confidence: 99%

A Resource-Efficient Multi-Function Embedded Eye Tracker System Implemented on FPGA

Khaki,

Yang,

Kim

et al. 2023

IEEE Access

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In this paper, a multi-function embedded eye tracker system (ETS) has been presented and FPGA implementation of that has been described. The image processing algorithm is based on morphological operations to detect centers of pupil (PC) and corneal glint (GC) so that gaze of the user can be estimated by establishing a mapping between obtained PC-GC vector and screen. Moreover, the proposed ETS is capable to detect pupil size which is vitally important in understanding consciousness of the user since it varies in response to light condition. Taking advantage of optimum system design and parallel signal processing on high-speed digital units of the FPGA, the processing time of 1.8 ms was achieved that allows operation of the system up to 500 fps. The results of test with 176 × 120 images demonstrate maximum vertical or horizontal error of ±1 pixel. Experimental results imply versatility of the proposed low-power and highaccuracy ETS for real-time and accurate applications such as medical diagnosis and human-computer interface. INDEX TERMSEmbedded system, Field programmable gate arrays, Gaze tracking, Hardware acceleration, Morphological operations.

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AES Algorithm for the Next Generation of 5G Network Encryption Standards

Manojkumar

Tamilarasi²,

Mahalakshmi

et al. 2023

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

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RE-PUPIL: resource efficient pupil detection system using the technique of average black pixel density

Cited by 25 publications

References 23 publications

The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

A Resource-Efficient Multi-Function Embedded Eye Tracker System Implemented on FPGA

AES Algorithm for the Next Generation of 5G Network Encryption Standards

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