A robust eye detection method based on multi-neighborhood block with weight and SVM on gray image

2013 ◽  
Author(s):  
Liming Yan ◽  
Xinzhu Sang
Keyword(s):  
Detection Method ◽  
Eye Detection ◽  
Gray Image

A robust eye detection method using combined binary edge and intensity information

2006 ◽  
Vol 39 (6) ◽  
pp. 1110-1125 ◽  
Author(s):  
Jiatao Song ◽  
Zheru Chi ◽  
Jilin Liu
Keyword(s):  
Detection Method ◽  
Eye Detection ◽  
Intensity Information ◽  
Binary Edge

scholarly journals An efficient hybrid eye detection method

2016 ◽  
Vol 24 ◽  
pp. 1586-1603 ◽  
Author(s):  
Mingxin YU ◽  
Yingzi LIN ◽  
Xiangzhou WANG
Keyword(s):  
Detection Method ◽  
Eye Detection

Rapid eye detection method for non-glasses type 3D display on portable devices

2010 ◽  
Vol 56 (4) ◽  
pp. 2498-2505 ◽  
Author(s):  
Byeoung-su Kim ◽  
Hyun Lee ◽  
Whoi-Yul Kim
Keyword(s):  
Detection Method ◽  
Portable Devices ◽  
3D Display ◽  
Eye Detection

An eye detection method robust to eyeglasses for mobile iris recognition

2017 ◽  
Vol 67 ◽  
pp. 178-188 ◽  
Author(s):  
Yujin Jung ◽  
Dongik Kim ◽  
Byungjun Son ◽  
Jaihie Kim
Keyword(s):  
Iris Recognition ◽  
Detection Method ◽  
Eye Detection

scholarly journals Estimation of object location probability for object detection using brightness feature only

2019 ◽  
Vol 9 (6) ◽  
pp. 5227
Author(s):  
Hyun Jun Park ◽  
Kwang Baek Kim
Keyword(s):  
Object Detection ◽  
Success Rate ◽  
Detection Method ◽  
Experimental Results ◽  
New Method ◽  
Object Location ◽  
Detection Methods ◽  
Gray Image ◽  
Probability Map

Most existing object detection methods use features such as color, shape, and contour. If there are no consistent features can be used, we need a new object detection method. Therefore, in this paper, we propose a new method for estimating the probability that an object can be located for object detection and generating an object location probability map using only brightness in a gray image. To evaluate the performance of the proposed method, we applied it to gallbladder detection. Experimental results showed 98.02% success rate for gallbladder detection in ultrasonogram. Therefore, the proposed method accurately estimates the object location probability and effectively detected gallbladder.

Eye Detection Using Color, Haar Features, and Efficient Support Vector Machine

2012 ◽  
pp. 286-309
Author(s):  
Shuo Chen ◽  
Chengjun Liu
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Detection Method ◽  
Support Vector ◽  
Grand Challenge ◽  
Detection Accuracy ◽  
Eye Detection ◽  
Color Information ◽  
Haar Features ◽  
Improved Performance

Eye detection is an important initial step in an automatic face recognition system. Though numerous eye detection methods have been proposed, many problems still exist, especially in the detection accuracy and efficiency under challenging image conditions. The authors present a novel eye detection method using color information, Haar features, and a new efficient Support Vector Machine (eSVM) in this chapter . In particular, this eye detection method consists of two stages: the eye candidate selection and validation. The selection stage picks up eye candidates over an image through color information, while the validation stage applies 2D Haar wavelet and the eSVM to detect the center of the eye among these candidates. The eSVM is defined on fewer support vectors than the standard SVM, which can achieve faster detection speed and higher or comparable detection accuracy. Experiments on Face Recognition Grand Challenge (FRGC) database show the improved performance over existing methods on both efficiency and accuracy.

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