Eye Detection Algorithm on Facial Color Images

Nasiri, Jalal A.; Khanchi, Sara; Pourreza, Hamid Reza

doi:10.1109/ams.2008.55

Cited by 19 publications

(6 citation statements)

References 10 publications

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“…Finally threshold and geometric test are applied on these candidates. The detection rate of 50 people images was about 98.5%, and average execution time was 1.395 in second (5). Nobuo Funabiki, extracted eye contour from face image using deformable template matching, template parameters were optimized by using local search method in addition to hill climbing and tabu search to extract the contour of eye.…”

Section: Related Workmentioning

confidence: 99%

Eye Detection using Helmholtz Principle

Ahmed¹

2019

Baghdad Sci.J

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Eye Detection is used in many applications like pattern recognition, biometric, surveillance system and many other systems. In this paper, a new method is presented to detect and extract the overall shape of one eye from image depending on two principles Helmholtz & Gestalt. According to the principle of perception by Helmholz, any observed geometric shape is perceptually "meaningful" if its repetition number is very small in image with random distribution. To achieve this goal, Gestalt Principle states that humans see things either through grouping its similar elements or recognize patterns. In general, according to Gestalt Principle, humans see things through general description of these things. This paper utilizes these two principles to recognize and extract eye part from image. Java programming language and OpenCV library for image processing are used for this purpose. Good results are obtained from this proposed method, where 88.89% was obtained as a detection rate taking into account that the average execution time is about 0.23 in seconds.

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Section: Related Workmentioning

confidence: 99%

Eye Detection using Helmholtz Principle

Ahmed¹

2019

Baghdad Sci.J

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“…There are two geometrical tests being adapted from [16] to filter false positive centroids for the irises. The geometrical tests include the following:…”

Section: Geometrical Tests and Verificationmentioning

confidence: 99%

Hybrid method of iris detection based on face localization

See

Noor

Rijal

2014

TENCON 2014 - 2014 IEEE Region 10 Conference

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This paper presents a hybrid method of iris detection system based on edge detection and Hough Transform, with the help of skin segmentation in face detection algorithm, Golden Face Ratio and geometric definition. The algorithm starts with skin detection using Gaussian mixture model to find the bounding area of the face. Next, the facial area is used to mask the area for eye segmentation process. After the potential eye regions have been obtained, the centroids of the potential eyes and the edge detection process is performed. Then, using the obtained information, the coordinates of the irises are determined through the circular Hough Transform formula. For verification, several geometrical tests are performed onto the centroids to find out the best pair to be the irises of the eyes. The output image is displayed with the face bounded and its irises marked.

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“…In this work, the illumination based method [11,13,14], which is one of the popular methods, was used to identify eye regions in color images since this technique does not need to spend time to train the system such as many machine learning algorithms. However, most of the eye detection techniques mentioned above are able to use only for frontal facial images.…”

Section: Introductionmentioning

confidence: 99%

Development of a geometrical algorithm for eye detection in color images

Phromsuthirak

Umchid

2012

The 5th 2012 Biomedical Engineering International Conference

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The applications of eye detection have grown significantly during the past decade. The objective of this paper was to develop a new technique for eye detection in color facial images. The implementation of this technique was composed of three steps. First step is using skin color information to detect a face in color images. The face region would be segmented from the background. Next step, illumination based method (chrominance components and luma component) was employed to find the possible location of the eyes. Finally, to locate the exact position of the eyes, the flexible thresholding method and new eye geometrical tests were utilized. The proposed technique was tested using Aberdeen facial images from Psychological Image Collection at Stirling (PICS image database). The simulation results indicate the detection rate of 93.2% for frontal facial images and 85.7% for rotational facial images.

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Eye Detection Algorithm on Facial Color Images

Cited by 19 publications

References 10 publications

Eye Detection using Helmholtz Principle

Eye Detection using Helmholtz Principle

Hybrid method of iris detection based on face localization

Development of a geometrical algorithm for eye detection in color images

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