Title :
Wavelet Maxima and Moment Invariants Based Iris Feature Extraction
Author :
Nabti, Makram ; Bouridane, Ahmed
Author_Institution :
Queens Univ. Belfast, Belfast
fDate :
Sept. 16 2007-Oct. 19 2007
Abstract :
Iris recognition is one of the most reliable personal identification methods and is becoming the most promising technique for high security. In this paper, we propose an efficient method for personal iris identification by investigating iris textures that have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, we present a new approach to making a feature vector compact and efficient by using wavelet transform (wavelet maxima components), and moment invariants. The proposed scheme is invariant to translation, rotation, and scale changes. Experimental results have shown that the proposed system could be used for personal identification in an efficient and effective manner.
Keywords :
biometrics (access control); edge detection; feature extraction; image texture; security; feature vector; high security; iris feature extraction; iris recognition; iris textures; moment invariants; personal identification; personal iris identification; wavelet maxima components; wavelet transform; Biometrics; Consumer electronics; Feature extraction; Fingerprint recognition; Gabor filters; Humans; Image edge detection; Iris recognition; Signal resolution; Wavelet transforms; iris feature extraction; moment invariants; multiscale edge detection; wavelet maxima;
Conference_Titel :
Image Processing, 2007. ICIP 2007. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-1437-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2007.4379176