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Description: 一个由zafersavas于2008年完成的完全基于VC++6实现的人脸检测和人眼跟踪程序，通过设置相应的参数实现不同的功能。人脸跟踪中使用了camshift算法和Haar算法，眼睛检测中使用了自适应PCA算法和模板匹配算法，还支持文件和网络摄像头两种输入方式，经过试验，检测速度比较快和准确度也比较高。附带demo程序。 使用步骤： 菜单TrackEye Menu --> Tracker Settings 输入源Input Source: video 选择文件输入指定为： ..\Avis\Sample.avi 人脸检测算法（Face Detection Algorithm）: Haar Face Detection Algorithm 选中 “Track also Eyes” checkBox 眼睛检测算法（Eye Detection Algorithm）: Adaptive PCA 取消选择 “Variance Check” Number of Database Images: 8 Number of EigenEyes: 5 Maximum allowable distance from eyespace: 1200 Face width/eye template width ratio: 0.3 ColorSpace type to use during PCA: CV_RGB2GRAY 瞳孔检测设置（Settings for Pupil Detection） Check “Track eyes in details” and then check “Detect also eye pupils”. Click “Adjust Parameters” button: Enter “120” as the “Threshold Value” Click “Save Settings” and then click “Close” Settings for Snake Check “Indicate eye boundary using active snakes”. Click “Settings for snake” button: Select ColorSpace to use: CV_RGB2GRAY Select Simple thresholding and enter 100 as the “Threshold value” Click “Save Settings” and then click “Close”
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Size: 204022 |
Author: sichuanlu |
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Description: 人眼虹膜识别的图像预处理，包括边缘检测，虹膜定位等，可以实现瞳孔的精定位-The human eye iris recognition of image pre-processing, including edge detection, iris positioning, you can realize the essence of pupil positioning
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Size: 2197504 |
Author: kongqiang |
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Description: 在AdaBoost检测人脸的基础上，精确找到人脸中眼睛瞳孔中心的程序。-In AdaBoost Face Detection Based on the precision to find human faces in the eye pupil center procedures.
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Size: 1458176 |
Author: 李查德 |
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Description: 人脸检测+眼睛检测（模版匹配）+瞳孔检测+嘴唇检测（cameshift）-face detection+ eye detection( template match)+pupil detection+lip detection (camshift)
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Size: 48332800 |
Author: briend |
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Description: FAST PUPIL LOCATION FOR BETTER IRIS DETECTION
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Size: 133120 |
Author: mannou |
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Description: iris segmentation ,pupil detection with out assuming it s circular or elliptical
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Size: 655360 |
Author: hamed |
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Description: 改程序对于输入的虹膜图像，采用圆形模板检测的方法，定位瞳孔内的一点-Reform program for the input iris image, using a circular template detection method, locate the point within the pupil
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Size: 2273280 |
Author: smli |
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Description: 人眼虹膜识别的图像预处理，包括边缘检测，虹膜定位等，可以实现瞳孔的精定位-Recognition of human iris image preprocessing, including edge detection, such as iris, pupil of the fine positioning can be achieved
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Size: 241664 |
Author: 赵鑫 |
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Description: Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value) of each column and
row. Then get each row and column that has the maximum number of these black pixels.
Then determine the center by simple calculation according to the image coordinate to set it
correct on the image, consequently we can determine the radius of the pupil. Thus we can find
the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to
locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms
of circle and ellipse.
Platform: |
Size: 1024 |
Author: boss |
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Description: Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value) of each column and
row. Then get each row and column that has the maximum number of these black pixels.
Then determine the center by simple calculation according to the image coordinate to set it
correct on the image, consequently we can determine the radius of the pupil. Thus we can find
the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to
locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms
of circle and ellipse.
Platform: |
Size: 2048 |
Author: boss |
Hits:
Description: Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value) of each column and
row. Then get each row and column that has the maximum number of these black pixels.
Then determine the center by simple calculation according to the image coordinate to set it
correct on the image, consequently we can determine the radius of the pupil. Thus we can find
the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to
locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms
of circle and ellipse.
Platform: |
Size: 1024 |
Author: boss |
Hits:
Description: Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value) of each column and
row. Then get each row and column that has the maximum number of these black pixels.
Then determine the center by simple calculation according to the image coordinate to set it
correct on the image, consequently we can determine the radius of the pupil. Thus we can find
the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to
locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms
of circle and ellipse.
Platform: |
Size: 1024 |
Author: boss |
Hits:
Description: Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value) of each column and
row. Then get each row and column that has the maximum number of these black pixels.
Then determine the center by simple calculation according to the image coordinate to set it
correct on the image, consequently we can determine the radius of the pupil. Thus we can find
the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to
locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms
of circle and ellipse.
Platform: |
Size: 1024 |
Author: boss |
Hits:
Description: Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value) of each column and
row. Then get each row and column that has the maximum number of these black pixels.
Then determine the center by simple calculation according to the image coordinate to set it
correct on the image, consequently we can determine the radius of the pupil. Thus we can find
the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to
locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms
of circle and ellipse.
Platform: |
Size: 1024 |
Author: boss |
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Description: 基于OpenCV的Hough变换结合Canny边缘检测的人眼瞳孔检测算法。-Combination of the Canny edge detection of the human eye pupil detection algorithm based on OpenCV Hough transform.
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Size: 733184 |
Author: 食草堂 |
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Description: 采用linux下的opencv进行人眼瞳孔检测-Opencv under linux pupil detection of the human eye
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Size: 129024 |
Author: |
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Description: iris pupil detection
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Size: 1024 |
Author: dhaval |
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Description: Written in VC++, used for the pupil detection in eye image, based on the theory of Hough Transformation
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Size: 2048 |
Author: Lei Zhang |
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Description: 是用于检测瞳孔的python代码，只能检测单个(Python code for pupil detection)
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Size: 1024 |
Author: 嘿嘿1232 |
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Description: 瞳孔检测，可以快速准确的定位到瞳孔，并且显示出黑白二值图像(Pupil detection, can quickly and accurately locate the pupil, and display the black and white binary image)
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Size: 190464 |
Author: LIN东 |
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