The week's pick
Reseach Article
Classifying Iris Image based on Hierarchical Visual Codebook and Encryption using Bio-Chaotic Algorithm (BCA)
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 2 - Number 7 |
| Year of Publication: 2015 |
| Authors: Rashmi M. Mhatre, Deeksha Bhardwaj |
10.5120/cae2015651805
|
Rashmi M. Mhatre, Deeksha Bhardwaj . Classifying Iris Image based on Hierarchical Visual Codebook and Encryption using Bio-Chaotic Algorithm (BCA). Communications on Applied Electronics. 2, 7 ( August 2015), 22-27. DOI=10.5120/cae2015651805
Abstract
The classification of the iris image under the fake or real and also adding security to it by encrypting it provides double the security. Iris recognition system can undergo the security attacks which can result into the fraudulent identity authentication. The attacker therefore will try to develop the methods which will spoof the iris biometrics. Therefore it becomes difficult to develop the recognition system which will be attack proof. The one of the solution to this is iris liveness detection, where fake and the real iris images are classified and detected. As it is known that the anti-virus industry establishes the computer as well as internet virus databases to tackle the problem of viruses, malwares etc ,these database is dynamically get updated as they are share via public domain and can use this concept to tackle the fake iris images by preparing iris database. So, in this project will try to classify the iris images into the fake and real images, and store those into database along with this we are going to use the cryptographic algorithm to achieve the security. The use of the bio-chaotic stream cipher will help to encrypt the iris images and store them securely with the help of biometric key and bio-chaotic function.
References
- Sun, Zheng, et. al. Iris Image Classification Based on Hierarchical Visual Codebook (2014):1-1.
- Alg hamdi, Abdullah Sharaf, et al. “Bio-chaotic stream cipher –based iris image encryption.” Computational science and engineering, 2009. CSE’09. International conference on. Vol.2. IEEE, 2009
- M. Sunder and A. Ross, “Iris image retrieval based on macro-features,” in Proc. ICPR, Istanbul, Turkey, 2010, pp. 1318–1321.
- Z. Sun and T. Tan, “Ordinal measures for iris recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 12, pp. 2211–2226, Dec. 2009
- E. Lee, K. Park, and J. Kim, “Fake iris detection by using purkinje image,” in Proc. ICB, Hong Kong, China, 2006, pp. 397–403.
- X. He, Y. Lu, and P. Shi, “A fake iris detection method based on FFT and quality assessment,” inProc. Chinese Conf. Pattern Recognition, Beijing, China, 2008, pp. 316–319.
- Z. Wei, X. Qiu, Z. Sun, and T. Tan, “Counterfeit iris detection based on texture analysis,” inProc. ICPR, Tampa, FL, USA, 2008, pp. 1–4.
- Z. He, Z. Sun, T. Tan, and Z. Wei, “Efficient iris spoof detection via boosted local binary patterns,” inProc. ICB, Alghero, Italy, 2009, pp. 1080–1090.
- H. Zhang, Z. Sun, and T. Tan, “Contact lens detection based on weighted LBP,” in Proc. ICPR, Istanbul, Turkey, 2010, pp. 4279–4282.
- S. Gutta, H. Wechsler, and P. Phillips, “Gender and ethnic classification of face images,” inProc. FG, Nara, Japan, 1998, pp. 194–199.
- G. Shakhnarovich, P. Viola, and B. Moghaddam, “A unified learning framework for real time face detection and classification,” in Proc. FG, Washington, DC, USA, 2002, pp. 14–21.
- X. Lu and A. Jain, “Ethnicity identification from face images,” in Proc. SPIE Defense and Security Symp., vol. 5404. 2004, pp. 114–123.
- X. Qiu, Z. Sun, and T. Tan, “Global texture analysis of iris images for ethnic classification,” in Proc. ICB, Hong Kong, China, 2006, pp. 411–418.
- X. Qiu, Z. Sun, and T. Tan, “Learning appearance primitives of iris images for ethnic classification,” inProc. ICIP,vol.2.SanAntonio, TX, USA, 2007, pp. 405–408.
- H. Zhang, Z. Sun, T. Tan, and J. Wang, “Ethnic classification based on iris images,” inProc. Chinese Conf. Biometric Recognition, Beijing, China, 2011, pp. 82–90.
- J. Lyle, P. Miller, S. Pundlik, and D. Woodard, “Soft biometric classification using periocular region features,” in Proc. BTAS, Washington, DC, USA, 2010, pp. 1–7.
- L. Yu, D. Zhang, K. Wang, and W. Yang, “Coarse iris classification using box-counting to estimate fractal dimensions,” Pattern Recognit., vol. 38, no. 11, pp. 1791–1798, 2005.
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