Quality parameter assessment on iris images

• Autorzy: Makinana S. , Malumedzha T. , Nelwamondo F. V.

Identyfikatory

DOI

10.2478/jaiscr-2014-0022

• Języki publikacji: EN

Abstrakty

EN

Iris biometric for personal identification is based on capturing an eye image and obtaining features that will help in identifying a human being. However, captured images may not be of good quality due to variety of reasons e.g. occlusion, blurred images etc. Thus, it is important to assess image quality before applying feature extraction algorithm in order to avoid insufficient results. Poor quality images may affect the recognition as they have few sufficient feature information. Moreover, existing quality measures focuses on parameters or factors than feature information. In this paper, iris quality assessment research is extended by analysing the effect of entropy, contrast, area ratio, occlusion, blur, dilation and sharpness of an iris image which determines the iris size, amount of information and clearness of the features. A weighting method based on principal component analysis (PCA) is proposed to determine the influence each parameter has on the quality score. To test the proposed technique; Chinese Academy of Science Institute of Automation (CASIA), Internal Collection (IC) and University of Beira Interior (UBIRIS) databases are used. A conclusion is drawn that the combination of blur, dilation and sharpness parameters have the most influence in the quality of the image as they weighed more than other parameters

Słowa kluczowe

EN

iris; image; eye; sharpness parameters; identifying

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2014
• Tom: Vol. 4, No. 1
• Strony: 21--30
• Opis fizyczny: Bibliogr. 18 poz., rys.

Twórcy

autor

Makinana S.

• CSIR , Modeling and Digital Science, P O Box 395, Pretoria, South Africa

autor

Malumedzha T.

• CSIR , Modeling and Digital Science, P O Box 395, Pretoria, South Africa

autor

Nelwamondo F. V.

• CSIR , Modeling and Digital Science, P O Box 395, Pretoria, South Africa

Bibliografia

• [1] Gulmire, K. and Ganorkar,S., Iris recognition using Gabor wavelet., International Journal of Engineering, 1(5), 2012.
• [2] Masek, L.: Recognition of human iris patterns for biometric identification. PhD thesis.
• [3] Ma, L., Tan, T., Wang, Y. and Zhang, D.,:Personal identification based on iris texture analysis., Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(12):1519– 1533, 2003.
• [4] Daugman, J.:How iris recognition works., Circuits and Systems for Video Technology, IEEE Transactions on, 14(1):21–30, 2004.
• [5] Fatukasi,O., Kittler, J., and Poh, N., :Quality controlled multi-modal fusion of biometric experts., In Progress in Pattern Recognition, Image Analysis and Applications, pages 881–890. Springer, 2007.
• [6] Kalka,N. D., Dorairaj, V.,Shah,Y. N., Schmid, N. A. and Cukic B.,: Image quality assessment for iris biometric., In Proceedings of the 24th Annual Meeting of the Gesellscha it Klassikation, pages 445–452. Springer, 2002.
• [7] Sandre, S-L and Stevens, M. and Mappes, J.,: The effect of predator appetite, prey warning coloration and luminance on predator foraging decisions, Behaviour, vol.147, No. 9., 1121–1143, BRILL, 2010
• [8] Du, Y. and Belcher, C. and Zhou, Z. and Ives, R.,: Feature correlation evaluation approach for iris feature quality measure, Signal processing, Vol. 90, No. 4, 1176–1187, Elsevier, 2010
• [9] Nill, N. B, IQF (Image Quality of Fingerprint) Software Application, The MITRE Corporation, 2007
• [10] Crete, F., Dolmiere,T., Ladret, P. and Nicolas, M.: The blur effect: perception and estimation with a new no-reference perceptual blur metric., Human Vision and Electronic Image in XII, 6492:64920I, 2007.
• [11] Li, Y.H., Savvides, M.: An automatic iris occlusion estimation method based on high-dimensional density estimation., Pattern Analysis Machine Intelligence, IEEE Transactions on, pp 784-9-6,35(4), 2013.
• [12] Yalamanchili, R. K.: Occlussion Metrics,West virginia University, 2011
• [13] Bieroza, M. and Baker, A. and Bridgeman, J.,: Classification and calibration of organic matter fluorescence data with multiway analysis methods and artificial neural networks: an operational tool for improved drinking water treatment, Environmetrics, Vol. 22, No.3, 256–270,Wiley Online Library, 2011
• [14] Jeong, D. H. and Ziemkiewicz, C. and Ribarsky, W. and Chang, R. and Center, C. V.,:Understanding Principal Component Analysis Using a Visual Analytics Tool, Charlotte Visualization Center, UNC Charlotte, 2009
• [15] Suhr, D. D.:Principal component analysis vs. Exploratory factor analysis, SUGI 30 Proceedings, 203–230, 2005
• [16] Proenc¸a, H. and Alexandre, L.A., UBIRIS: A noisy iris image database, International Conference on Image Analysis and Processing, 2005
• [17] Chinese Academy of Sciences Institute of Automation., CASIA Iris Database,Online: http://biometrics.idealtest.org/dbDetailForUser .do?id=4, 2012
• [18] Fairchild M, D:Color Appearance Models, Slides from a tutorial at the IST/SID 12th Color Imaging Conference, 2004