Biometrics is the new technology for body measurements and calculations that is use to identifying a person. It signifies to metrics related to human physiological or behavioral characteristics. Many specific physiological and behavioral parts, personal characteristics have been suggested and used for biometric security scheme [1]. Any Biometric system comprises of four modules: sensor module, feature extraction module, database module and matching module. Out of all these module feature extraction module of any recognition system plays an important role in recognizing the particular objects with same set of images [3]. This paper presents an analysis on the use of the newly introduced modern and popular key-points feature extracting tools and methodologies that can be applicable in the biometric domain. The implementation is carried out using MATLAB programming environment and tested on CASIA database for Iris and FVC2004 DB3_A for Fingerprint.

Biometric; Iris; Fingerprint; Feature;Templte; Matching

[1] A. K. Jain, Fellow, IEEE, A. Ross, Member, IEEE, and S. Prabhakar, Member, IEEE, “An Introduction to Biometric Recognition”, IEEE transactions on circuits and systems for video technology, vol. 14, no. 1, january 2004.
[2] S Prakash and P Gupta, “An Efficient Ear Localization Technique”, Image and Vision Computing, 30(1), pp. 38-50, (2012).
[3] O P Sharma and J Sheetlani, “Biometric based authentication system: a survey”, International Journal of Current Advanced Research Volume 6; Issue 7; July 2017; Page No. 4487-4492
[4] D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision 60(2): 91-110, 2006.
[5] H. Bay, T. Tuytelaars, and L. V. Gool, “SURF: Speeded up Robust Features”, Journal of Computer vision and image understanding 110 (3): 346-359, 2008.
[6] P. M. Panchal, S. R. Panchal, S. K. Shah, "A Comparison of SIFT and SURF", International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 2, April 2013
[7] D. Mistry, A. Banerjee, “Comparison of Feature Detection and Matching Approaches: SIFT and SURF”, Global Research and Development Journal for Engineering, Volume 2, Issue 4, March 2017.
[8] Guerrero, Maridalia, “A Comparative Study of Three Image Matcing Algorithms: Sift, Surf, and Fast”, (2011). All Graduate Theses and Dissertations. Paper 1040.
[9] L. Masek, “Recognition of Human Iris Patterns for Biometric Identification”, The University of Western Australia 2003 [http://www.csse.uwa.edu.au/~pk/studentprojects/libor/LiborMasekThesis.pdf].
[10] R. Wildes, “Iris Recognition: An Emerging Biometric Technology”, Proc IEEE 1997, 85:1348-1363.
[11] Chinese Academy of Sciences Institute of Automation (CASIA) iris database [http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp]
[12] R. Kabbani,“Selecting Most Efficient Arabic OCR Features Extraction Methods Using Key Performance Indicators”, 2nd International Conference on Communications, Computing and Control Applications (CCCA), 2012.
[13] Yakubu Ajiji Makeri, “The role of Cyber Security and Human-Technology Centric for Digital Transformation”, International Journal of Scientific Research in Computer Science and Engineering (IJSRCSE), Vol.6, Issue.6, pp.53-59, December (2018).