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

Sadhana ◽  
2021 ◽  
Vol 46 (3) ◽  
Author(s):  
S NAVANEETHAN ◽  
N NANDHAGOPAL
Keyword(s):  
Detection System ◽  
Black Pixel ◽  
Pupil Detection

Human Eye Pupil Detection System for Different IRIS Database Images

2021 ◽  
Vol 18 (4) ◽  
pp. 1239-1242
Author(s):  
N. Nandhagopal ◽  
S. Navaneethan ◽  
V. Nivedita ◽  
A. Parimala ◽  
Dinesh Valluru
Keyword(s):  
Near Infrared ◽  
Detection System ◽  
Recognition Rate ◽  
Vital Role ◽  
Center Of Gravity ◽  
Bright Spot ◽  
Threshold Method ◽  
Human Eye ◽  
Pupil Detection ◽  
Suppression Process

The pupil detection system plays a vital role in ophthalmology diagnosis equipments because pupil has a center place of human eye to locate the exact position. To identify the exact human eye pupil region in near infrared (NIR) images, this work proposes the Center of gravity method and its real time FPGA hardware implementation. The proposed work involves global threshold method to segment the pupil region from human eye and the bright spot suppression process removes the light reflections on the pupil due to the IR (Infra red) rays then the morphology dilation process removes unnecessary black pixels other than pupil region on the image. Finally, center of gravity (COG) method provides the exact pupil center coordinate and radius of the human eye. CASIA IRIS V4 and UBIRIS iris database images used in this work and achieved 90-95% of recognition rate.

Pupil Detection in Facial Images with Using Bag of Pixels

2012 ◽  
Vol 468-471 ◽  
pp. 2941-2948
Author(s):  
Mohammad Ali Azimi Sotudeh ◽  
Hasan Ziafat ◽  
Said Ghafari
Keyword(s):  
Eye Tracking ◽  
Detection System ◽  
Horizontal Projection ◽  
Distinctive Features ◽  
Region Detection ◽  
Face Region ◽  
Pupil Detection ◽  
Intersection Method ◽  
Facial Images ◽  
Eye Region

To detect and track eye images, distinctive features of user eye are used. Generally, an eye-tracking and detection system can be divided into four steps: Face detection, eye region detection, pupil detection and eye tracking. To find the position of pupil, first, face region must be separated from the rest of the image using bag of pixels, this will cause the images background to be non effective in our next steps. We used from horizontal projection, to separate a region containing eyes and eyebrow. This will result in decreasing the computational complexity and ignoring some factors such as bread. Finally, in proposed method points with the highest values of are selected as the eye candidate's. The eye region is well detected among these points. Color entropy in the eye region is used to eliminate the irrelevant candidates. With a pixel of the iris or pupil can be achieved center of pupil. To find the center of pupil can be used line intersection method in the next step, we perform eye tracking. The proposed method achieve a correct eye detection rate of 97.3% on testing set that gathered from different images of face data. Moreover, in the case of glasses the performance is still acceptable.

An FPGA-Based Real-Time Human Eye Pupil Detection System Using E2V Smart Camera

2019 ◽  
Vol 16 (2) ◽  
pp. 649-654
Author(s):  
S. Navaneethan ◽  
N. Nandhagopal ◽  
V. Nivedita
Keyword(s):  
Real Time ◽  
Detection System ◽  
Detection Algorithm ◽  
Video Frame ◽  
Smart Camera ◽  
Verilog Hdl ◽  
Real Time System ◽  
Human Eye ◽  
Pupil Detection ◽  
Main Motive

Threshold based pupil detection algorithm was found tobe most efficient method to detect human eye. An implementation of a real-time system on an FPGA board to detect and track a human's eye is the main motive to obtain from proposed work. The Pupil detection algorithm involved thresholding and image filtering. The Pupil location was identified by computing the center value of the detected region. The proposed hardware architecture is designed using Verilog HDL and implemented on aAltera DE2 cyclone II FPGA for prototyping and logic utilizations are compared with Existing work. The overall setup included Cyclone II FPGA, a E2V camera, SDRAM and a VGA monitor. Experimental results proved the accuracy and effectiveness of the hardware realtime implementation as the algorithm was able to manage various types of input video frame. All calculation was performed in real time. Although the system can be furthered improved to obtain better results, overall the project was a success as it enabled any inputted eye to be accurately detected and tracked.

scholarly journals A video-based eye pupil detection system for diagnosing bipolar disorder

2013 ◽  
Vol 21 ◽  
pp. 2367-2377 ◽  
Author(s):  
Gökay AKINCI ◽  
Ediz POLAT ◽  
Orhan Murat KOÇAK
Keyword(s):  
Bipolar Disorder ◽  
Detection System ◽  
Pupil Detection

On designing an unconstrained tri-band pupil detection system for human identification

2015 ◽  
Vol 26 (7-8) ◽  
pp. 1007-1025 ◽  
Author(s):  
Cameron Whitelam ◽  
Thirimachos Bourlai
Keyword(s):  
Detection System ◽  
Human Identification ◽  
Pupil Detection

Pupil Detection System Using Intensity Labeling Algorithm in Field Programmable Gate Array

2020 ◽  
Vol 17 (12) ◽  
pp. 5364-5367
Author(s):  
S. Baskaran ◽  
L. Mubark Ali ◽  
A. Anitharani ◽  
E. Annal Sheeba Rani ◽  
N. Nandhagopal
Keyword(s):  
Real Time ◽  
Detection System ◽  
Median Filter ◽  
Diagnostic Technique ◽  
Image Process ◽  
Eye Tracker ◽  
Eye Detection ◽  
Detection Techniques ◽  
Pupil Detection ◽  
Labeling Algorithm

Pupil detection techniques are an essential diagnostic technique in medical applications. Pupil detection becomes more complex because of the dynamic movement of the pupil region and it’s size. Eye-tracking is either the method of assessing the point of focus (where one sees) or the orientation of an eye relative to the head. An instrument used to control eye positions and eye activity is the eye tracker. As an input tool for human-computer interaction, eye trackers are used in research on the visual system, in psychology, psycholinguistics, marketing, and product design. Eye detection is one in all the applications in the image process. This is very important in human identification and it will improve today’s identification technique that solely involves the eye detection to spot individuals. This technology is still new, only a few domains are applying this technology as their medical system. The proposed work is developing an eye pupil detection method in real-time, stable, using an intensity labeling algorithm. The proposed hardware architecture is designed using the median filter, segmentation using the threshold process, and morphology to detect pupil shape. Finally, an intensity Labeling algorithm is done to locate an exact eye pupil region. A Real-time FPGA implementation is done by Altera Quartus II software with cyclone IV FPGA.

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