..............................
..............................
..............................
Finger Knuckle Print Authentication Using AES
In general, the identification and verification are done by passwords, PIN number, etc., which can be easily cracked
by hackers. Biometrics is a powerful and unique too l based on the anatomical and behavioural character istics of the human
beings in order to prove their authentication. Secu rity is the most important thing in the world. Password is used for security,
but it does not provide the effective security. So biometrics can be used to provide the higher securi ty than the password.
Finger Knuckle Print (FKP) is a unique biometric an atomical feature for an individual person. Biometric systems are suffered
to a variety of attacks. In order to avoid these at tacks, the biometric combined cryptography is the m ajor tool. Bio.crypto
system is to provide the authentication as well as the confidentiality of the data. This paper presents biometric key, which is
generated from key points of FKP using k.means algo rithm and secret hash value also generated using Secure Hash Algorithm
(SHA) function, which is encrypted with the FKP ext racted key points by Symmetric Advanced Encryption Standard (AES)
algorithm. The key points extraction of FKP was der ived using Scale Invariant Feature Transform (SIFT) . Hence encrypted
secret hash value secures biometric data and the se cret value. The hash function protects the biometric data from malicious
tampering, and it provides error checking functiona lity.
[1] Badrinath G, Nigam A., and Gupta P., An Efficient Finger-Knuckle-Print based Recognition System Fusing SIFT and SURF Matching Scores, in Proceedings of the 13 th international conference on Information and communications security , Beijing, China, pp. 374-387, 2011
[2] Federal Information Processing Standards Publication 197., Advanced Encryption Standard (AES) , 2001.
[3] Ferrer M., Travieso C., and Alonso J., Using Hand Knuckle Texture for Biometric Identifications, IEEE A&E Systems Magazine , pp.23-27, 2006.
[4] George A., Efficient High Dimension Data Clustering using Constraint-Partitioning K- Means Algorithm, the International Arab Journal of Information Technology , vol. 10, no. 6, pp. 467-476, 2013.
[5] Hao F., Anderson R., and Daugman J., Combining Crypto with Biometrics Effectively, IEEE Transaction on Computers , vol. 55, no. 9, pp. 1081-1088, 2006 .
[6] Hong L., Jain A., and Bolle R., On-Line Fingerprint Verification, IEEE Transactions Pattern Analysis and Machine Intelligence , vol. 19, no. 4, pp. 302-314, 1997.
[7] Jain A. and Pankanti S. , Fingerprint-Based Fuzzy Vault: Implementation and Performance, IEEE Transactions on Information Forensics and Security , vol. 2, no. 4, pp. 744-747, 2007.
[8] Jain A., Ross A., and Pankanti S., Biometrics: A Tool for Information Security, IEEE Transactions on Information Forensics and Security, vol. 1, no. 2, pp. 125-143, 2006.
[9] Juels A. and Sudan M., A Fuzzy Vault Scheme, Designs, Codes Cryptography , vol. 38, no. 2, pp. 237-257, 2006.
[10] Juels A. and Wattenbeg M., A Fuzzy Commitment Scheme, in Proceedings of the 6 th ACM conference on Computer and Communications Security , Singapore, pp 28-36, 1999.
[11] Kumar A. and Passi A., Comparison and Combination of Iris Matchers for Reliable Personal Authentication, Pattern Recognition, vol. 23, no. 3, pp. 1016-1026, 2010.
[12] Kumar A. and Zhang D., Improving Biometric Authentication Performance from the User Quality, IEEE Transactions on Instrumentation And Measurement , vol. 59, no. 3, pp. 730-735, 2010.
[13] Kumar A. and Zhou Y., Personal Identification using Finger Knuckle Orientation Features, Electronics Letters , vol. 45, no. 20, pp. 1023- 1031, 2009.
[14] Kumar A. and Prathyusha V., Personal Authentication Using Hand Vein Triangulation and Knuckle Shape, IEEE Transactions on Image Processing , vol. 18, no. 9, pp. 2127-2136, 2009.
[15] Liu M., Jiang X., and Kot A., Efficient Fingerprint Search based on Database Clustering, Pattern Recognition , vol. 40, pp. 1793-1803, 2007.
[16] Lowe D., Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision , vol. 60, no. 2, pp. 91-110, 2004.
[17] Mahmud M., Khan M., Alghathbar K., Abdullah A., and Idris M., Intrinsic Authentication of Multimedia Objects Using Biometric Data Finger Knuckle Print Authentication Using AES and K.Means Algorithm 649 Manipulation, the International Arab Journal of Information Technology , vol. 9, no. 4, pp. 336- 342, 2012,.
[18] Meskine F. and Bahloul S., Privacy Preserving K-Means Clustering: A Survey Research, the International Arab Journal of Information Technology , vol. 9, no. 2, pp. 194-200, 2012.
[19] Ne ma B. and Ali H. , Multi Purpose Code Generation Using Fingerprint Images, the International Arab Journal of Information Technology , vol. 6, no. 4, pp. 418-423, 2009.
[20] Poly U FKP., Database: http://www4.comp.polyu.edu.hk/~biometrics/, last visited 2013.
[21] Qing L., Finger Knuckle Print Recognition based on SURF Algorithm, in Proceedings of the 8 th International Conference on Fuzzy Systems and Knowledge Discovery , Shanghai, pp. 1879-1883, 2011.
[22] Schneier B., Applied Cryptography Protocols, Algorithms , Wiley Publication, 1996.
[23] Stallings W., Cryptography and Network Security Principles and Practice , Prentice Hall, 2013.
[24] Uludag U., Pankanti S., Prabhakar S., and Jain A., Biometric Cryptosystems: Issues and Challenges, available at: http://www.cse.msu.edu/~jain/BiometricCryptosy stemsIssuesAndChallenges.pdf, last visited 2004.
[25] Wankou Y., Changyin S., and Zhongxi S., Finger-Knuckle-Print Recognition Using Gabor Feature and OLDA, in Proceedings of the 30 th Chinese Control Conference , Yantai, China, pp. 2975-2978, 2011.
[26] Wu X., Qi N., Wang K., and Zhang D., A Novel Cryptosystem based on Iris Key Generation, in Proceedings of the 4 th International Conference on Natural Computation , Jinan, pp. 53-56 2008.
[27] Yang G., Zhou G., Yin Y., and Yang X., K- Means Based Fingerprint Segmentation with Sensor Interoperability, avalible at: http://asp.eurasipjournals.com/content/pdf/1687- 6180-2010-729378.pdf, last visited 2010.
[28] Zhang L., Zhang L., and Zhang D., Finger- Knuckle-Print: A New Biometric Identifier, in Proceedings International Conference on Image Processing. Cairo, pp. 1981-1984, 2009.
[29] Zhang L., Zhang L., Zhang D., and Guo Z., Phase Congruency Induced Local Features for Finger-Knuckle-Print Recognition, Pattern Recognition , vol. 45, no. 7, pp. 2522-2531, 2012.
[30] Zhang L., Zhang L., Zhang D., and Zhu H., Ensemble of Local and Global Information for Finger-Knuckle-Print Recognition, Pattern Recognition , vol. 44, no. 9, pp. 1990-1998, 2011.
[31] Zhang L., Zhang L., Zhang D., and Zhu H., Online Finger-Knuckle-Print Verification for Personal Authentication, Pattern Recognition, vol. 43, no. 7, pp. 2560-2571, 2010. Muthukumar Arunachalam received his BE (ECE) and ME (Applied Electronics) degrees from Madurai Kamaraj University and Anna University in 2004 and 2006 respectively. Currently, he is pursuing a PhD in ECE at Kalasalingam University, India. He is Assistant Professor of Electronics and Communication Engineering, Kalasalingam University, Krishnankoil- 626126, India, where he has been since July 2007. H is area of interest is image processing, signal proces sing, biometrics and wireless communication. He is a life member of ISTE. Kannan Subramanian received his BE., ME., and PhD degrees from Madurai Kamaraj University, India in 1991, 1998 and 2005 respectively. He is Professor and Head of Electrical and Electronics Engineering, Kalasalingam University, Krishnankoil-626126, India, where he ha s been since July 2000. He was a visiting scholar in Iowa State University, USA (October 2006 September 2007) supported by the Department of Science and Technology, Government of India with BOYSCAST Fellowship. He is a Sr. Member of IEEE, Fellow of I E (I), Sr. Member in CSI, Fellow in IETE, Life member SSI and Life member of ISTE.