An effective iris location method with high robustness.

• Autorzy: Tang R. , Han J. , Zhang X.
• Języki publikacji: EN

Abstrakty

EN

Now, iris recognition is a topiocal issue. This paper focuses on iris location, which is an important stage in iris recognition, and develops a new method for that purpose. Use has been made of dilation operator in order to suppress the interference of eyelash, and support vector machines (SVM) classifier has been employed to identify the iris boundary. The results of experiment carried out on iris image database showed the method proposed to have an encouraging performance. Compared with the traditional methods, those of Daugman and Wildes, this method offers a higher correct location ratio and is more computationally efficient.

Słowa kluczowe

EN

iris location; boundary recognition; support vector machine; SVM

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2007
• Tom: Vol. 37, nr 3
• Strony: 295--303
• Opis fizyczny: Bibliogr. 9 poz.

Twórcy

autor

Tang R.

autor

Han J.

autor

Zhang X.

• Xi an Jiaotong University, School of Electronics and Information Engineering, 28 Xianning West Rd, X an 710049, P.R. China,

Bibliografia

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• [2] DAUGMAN J., The importance of being random: statistical principles of iris recognition, Pattern Recognition 36(2), 2003, pp. 279–91.
• [3] WILDES R.P., Iris recognition: an emerging biometric technology, Proceedings of the IEEE 85(9), 1997, pp. 1348–63.
• [4] MA L., TAN T.N., WANG Y.H., ZHANG D.X., Personal identification based on iris texture analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 2003, pp. 1519–33.
• [5] MA L., TAN T.N.,WANG Y.H., ZHANG D.X., Efficient iris recognition by characterizing key local variations, IEEE Transactions on Image Processing 13(6), 2004, pp. 739–50.
• [6] XIAOMEI LIU, BOWYER K.W., FLYNN P.J., Experimental evaluation of iris recognition, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 3, 2005, p. 158.
• [7] MASEK L., Recognition of Human Iris Patterns for Biometric Identification: http://www.csse.uwa.edu.au/~pk/studentprojects/libor/.
• [8] BURGES C.J.C., A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery 2(2), 1998, pp. 121–67
• [9] Center for Biometrics and Security Research, CASIA Iris Image Database: http://www.sinobiometrics.com.