Improved Hough transform for fast Iris detection

Abstract

Objectives: an important part of digital image processing is recognition of patterns. Iris recognition is an important application of the same. As a result of the fact that CHT lacks speed due to its high time complexity, it is useful to recognize valid regions and make these regions the only regions to process. Extracting a valid region from the image not only reduces the image storage and the quantity of operations, it also improves the speed of the process. The objective of the work is to describe a method for enhancing the speed of extracting circle i.e. iris from the image without compromising the accuracy of the technique. A modified version of Hierarchical Hough transform is also proposed which further reduces the time taken to detect iris. An exhaustive analysis is conducted for a large number of iris images and comparison results are shown. Method: The HT space is limited by dividing it into nine equal parts and then the center most space is extracted as the valid region. Then CHT and HHT transformations are applied on the valid region.Results: About 88.77% of maximum improvement and 87.989 % of average improvement is recorded in time of execution of proposed CHT in comparison to existing CHT. Whereas, run time of HHT and proposed HHT when compared, results show that the maximum performance rate is 86.649% and average performance rate is 46.81%. Conclusion: in case of HT base detection methods, execution speed is directly proportional to the number of edge pixels in input image. Restricting the number of pixels provides the advantage of considerably simplified computations, and hence faster algorithms. The work documents that an aggregate comparison of proposed transformations with existing transformations has achieved a faster yet accurate method to recognize iris.

Embedded face recognition system considers human eye gaze using glass-type platform

“…The eye pupil center is obtained by processing the eye image in the sub embedded module. Within the local and global region of interest (ROI), through the several image frames, average eye pupil center and radius is calculated by Hough transform [5]. Finally the determined pupil center is transmitted to main embedded module.…”

An approach to human iris recognition using quantitative analysis of image features and machine learning

“…Automated iris segmentation has been an attractive topic of research in the recent past [5,6], and many methods [7][8][9] have been proposed to solve the problem. The first automatic method was presented by Daugman [10] using an efficient integrodifferential operator, which is still utilized in today's most of the iris recognition systems.…”

“…Image processing techniques as the first step can be applied to extract the unique pattern from the image, and encode it [11][12][13][14][15][16][17][18][19][20]. The feature extraction is another important part of Iris recognition discussed in some researches [3,7,10]. The shape and texture features are useful for identifying the Iris region's geometric properties.…”

Iris recognition system based on fast iris localization and phase correlation mathching

“…[3]'deki çalışmada, irisin iç ve dış sınırlarını belirlemek için integro-farksal (integro-differential) operatör önerilmektedir. Kenar tespiti ve sonrasında dairesel Hough dönüşümünü kullanan yöntemler [4][5][6]'da önerilmiştir. İmge histogram tepelerini kullanarak iris sınırlarının kaba şekilde belirlenmesi ve sonrasında bulunan sınırın detaylandırılması [7]'de önerilmiştir.…”