DocumentCode :
260343
Title :
Constructing irislet: A new wavelet type which matched for iris image characteristics
Author :
Isnanto, R. Rizal ; Satoto, Kodrat Iman ; Windasari, Ike Pertiwi
Author_Institution :
Comput. Eng. Dept., Diponegoro Univ., Semarang, Indonesia
fYear :
2014
fDate :
28-30 May 2014
Firstpage :
232
Lastpage :
237
Abstract :
Iris has a unique pattern that can be used in biometric recognition. To extract the features of the iris, it can be done based on the textural characteristics of the iris pattern. One method is a texture-based feature extraction using wavelet. To construct a wavelet type which matched for a signal, in this case two-dimensional signal from the iris image, the necessary steps are quite complex. In this research, all stages of wavelet design are carried out, beginning from iris image data acquisition up to the finding of the new wavelet, which will then be referred to as irislet. There are 19 (nineteen) steps in the design of this wavelet. To do all the stages, several basic concepts are required: convolution, circular Hough transform, conversion into unwrapped polar image form, determining the profile of the 1-D line images, signal averaging, concept of Daubechies wavelet basis, calculating signal energy, least squares method, how to construct scaling and wavelet functions, as well as the cascade algorithm. The test results showed that the recognition implementation irislet shows recognition rate is 100% correct.
Keywords :
Hough transforms; convolution; feature extraction; image matching; image texture; iris recognition; least squares approximations; wavelet transforms; 1-D line images; Daubechies wavelet basis; biometric recognition; cascade algorithm; circular Hough transform; convolution; image matching; iris image characteristics; iris image data acquisition; irislet construction; least squares method; recognition implementation irislet; scaling function; signal averaging; textural characteristics; texture-based feature extraction; two-dimensional signal; unwrapped polar image form conversion; wavelet design type; wavelet functions; Communications technology; Equations; Feature extraction; Iris; Iris recognition; Mathematical model; Wavelet transforms; cascade algorithm; irislet; least squares method; scaling function; wavelet function;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Communication Technology (ICoICT), 2014 2nd International Conference on
Conference_Location :
Bandung
Type :
conf
DOI :
10.1109/ICoICT.2014.6914071
Filename :
6914071
Link To Document :
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