Improved SIFT Algorithm Based on Mixed Programming

Zuo, Dong; Wen, Tao; Li, Zhong Ke; Li, Zhan Liang

doi:10.4028/www.scientific.net/amr.926-930.3058

Cited by 1 publication

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The histogram covers eight bins, and each descriptor contains an array around the key point of our histogram. This generates a SIFT feature descriptor of (4 x 4 x 8 = 128) elements, while the vector of the descriptor is invariant to rotation, scaling, and lighting [16].…”

Section: Key Point Descriptormentioning

confidence: 99%

Iris Identification Based on the Fusion of Multiple Methods

Hashim

Al-Khalidy

2021

eijs

View full text Add to dashboard Cite

Iris recognition occupies an important rank among the biometric types of approaches as a result of its accuracy and efficiency. The aim of this paper is to suggest a developed system for iris identification based on the fusion of scale invariant feature transforms (SIFT) along with local binary patterns of features extraction. Several steps have been applied. Firstly, any image type was converted to grayscale. Secondly, localization of the iris was achieved using circular Hough transform. Thirdly, the normalization to convert the polar value to Cartesian using Daugman’s rubber sheet models, followed by histogram equalization to enhance the iris region. Finally, the features were extracted by utilizing the scale invariant feature transformation and local binary pattern. Some sigma and threshold values were used for feature extraction, which achieved the highest rate of recognition. The programming was implemented by using MATLAB 2013. The matching was performed by applying the city block distance. The iris recognition system was built with the use of iris images for 30 individuals in the CASIA v4. 0 database. Every individual has 20 captures for left and right, with a total of 600 pictures. The main findings showed that the values of recognition rates in the proposed system are 98.67% for left eyes and 96.66% for right eyes, among thirty subjects.

show abstract

Section: Key Point Descriptormentioning

confidence: 99%