DocumentCode :
2927980
Title :
Efficient iris recognition system based on dual boundary detection using robust variable learning rate Multilayer Feed Forward neural network
Author :
Baqar, Mohtashim ; Azhar, Sohaib ; Iqbal, Zeeshan ; Shakeel, Irfan ; Ahmed, Laeeq ; Moinuddin, Muhammad
Author_Institution :
Fac. of Eng., Sci. & Technol, Iqra Univ., Karachi, Pakistan
fYear :
2011
fDate :
5-8 Dec. 2011
Firstpage :
326
Lastpage :
330
Abstract :
This paper presents a novel approach towards iris recognition based on dual boundary (Pupil-Iris & Sclera-Iris) detection and then using a modified Multilayer Feed Forward neural network (MFNN) to perform an efficient automatic classification. The novelty of the work resides in the fact that the proposed method features the localization of the dual iris boundaries to be used as feature vector for classification. The process of information extraction starts by preprocessing the eye-image to remove specular highlight and then locating the pupil of the eye by using edge detection. The centroid of the detected pupil is chosen as the reference point for extracting the boundary points. The boundary points are recorded using radius vector functions approach. The proposed feature vector is obtained by concatenating the contour points of the Pupil-Iris boundary and the Sclera-Iris boundary which will yield a unique pattern named as Iris signature. The proposed method is translational and scale invariant. The classification is performed using the MFNN via a modified version of back-propagation algorithm which uses a time varying learning rate. The proposed system has been tested on moderate no of pictures taken from MMU iris database in the presence of additive noise for different values of signal-to-noise ratio (SNR). Experimental result for percentage recognition shows that the proposed method outperforms the single boundary method.
Keywords :
backpropagation; edge detection; feature extraction; feedforward neural nets; image classification; iris recognition; MMU iris database; automatic classification; backpropagation algorithm; boundary points; contour points; dual boundary detection; dual iris boundaries; edge detection; eye image; feature vector; information extraction; iris recognition system; iris signature; multilayer feedforward neural network; percentage recognition; pupil detection; pupil-iris boundary; radius vector functions approach; reference point; robust variable learning rate; scale invariant; sclera-iris boundary; signal-to-noise ratio; single boundary method; time varying learning rate; translational invariant; Feature extraction; Image edge detection; Iris recognition; Neural networks; Security; Signal to noise ratio; Support vector machine classification; Back-propagation Algorithm; Biometric Security; Iris recognition; Neural Networks; specular highlight;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Assurance and Security (IAS), 2011 7th International Conference on
Conference_Location :
Melaka
Print_ISBN :
978-1-4577-2154-0
Type :
conf
DOI :
10.1109/ISIAS.2011.6122841
Filename :
6122841
Link To Document :
بازگشت