Efficient segmentation technique for noisy frontal view iris images using Fourier spectral density

Signal, Image and Video Processing

Volumn 5, Issue 1, 2011, Pages 105-119

  Puhan, Niladri B ^a   Sudha, N ^a   Sivaraman Kaushalram, Anirudh ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

Fourier spectral density; Image; Iris recognition; Low complexity; Segmentation

Indexed keywords

BIOMETRICS; FOURIER TRANSFORMS; HOUGH TRANSFORMS; ITERATIVE METHODS; MATHEMATICAL OPERATORS; SPECTRAL DENSITY;

FOURIER; IMAGE; INTEGRO DIFFERENTIAL OPERATOR; IRIS RECOGNITION; IRIS RECOGNITION SYSTEMS; IRIS SEGMENTATION METHOD; LOW COMPLEXITY; SEGMENTATION TECHNIQUES;

IMAGE SEGMENTATION;

EID: 79551521684     PISSN: 18631703     EISSN: 18631711     Source Type: Journal    
DOI: 10.1007/s11760-009-0146-z     Document Type: Article

References (17)

1. Daugman J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148-1161 (1993).
2. Wildes R.: Iris recognition: an emerging biometric technology. Proc. IEEE 85, 1348-1363 (1997).
3. Ma L., Tan T., Wang Y., Zhang D.: Personal identification based on iris texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1519-1533 (2003).
4. Ma L., Tan T., Wang Y., Zhang D.: Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 13(6), 739-750 (2004).
5. Daugman J.: New methods in iris recognition. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 37(5), 1167-1175 (2007).
6. Rakshit, S., Monro, D. M.: Pupil shape description using Fourier series. In: Proceedings of the IEEE Workshop on Signal Processing Applications for Public Security and Forensics, pp. 1-4 (2007).
7. Arvacheh, E. M., Tizhoosh, H. R.: Iris segmentation: detecting pupil, limbus and eyelids. In: Proceedings of the IEEE International Conference on Image Processing, pp. 2453-2456 (2006).
8. Ross, A., Shah, S.: Segmenting non-ideal irises using geodesic active contours. In: Proceedings of the IEEE Symposium on Biometrics (2006).
9. James R. M. et al.: Iris on the move: acquisition of images for iris recognition in less constrained environments. Proc. IEEE 94(11), 1936-1947 (2006).
10. Stephanie A. C. S. et al.: On techniques for angle compensation in nonideal iris recognition. IEEE Trans. Syst. Man Cybern. Part B: Cybern 37(5), 1176-1190 (2007).
11. Proenca H., Alexandre L. A.: UBIRIS: a noisy iris image database. Lect. Notes Comput. Sci. 3617, 970-977 (2005).
12. Proenca H., Alexandre L. A.: Iris segmentation methodology for non-cooperative recognition. IEE Proc. Vis. Image Signal Process. 153(2), 199-205 (2006).
13. Pavlidis T.: Algorithms for Graphics and Image Processing. Computer Science Press, Rockville (1982).
14. Gonzalez, R. C., Woods, R. E.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs.
15. Camus, T., Wildes, R.: Reliable and fast eye finding in close-up images. In: Proceedings of the 16th International Conference on Pattern Recognition, pp. 389-394 (2002).
16. Martin-Roche D., Sanchez-Avila C., Sanchez-Reillo R.: Iris recognition for biometric identification using dyadic wavelet transform zero-crossing. IEEE Aerosp. Electron. Syst. Mag. 17(10), 3-6 (2002).
17. Tuceryan M.: Moment based texture segmentation. Pattern Recognit. Lett. 15, 659-668 (1994).