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Introduction Recognizing a person can be done either on the basis of behavioral or physical characteristics in biometric automated methods. There are many behavioral attributes that can be voice, iris, fingerprints, and face recognition. Due to increase in fraud act from forgeries, the need for developing such secure systems for authorizing the right person has increased and these systems needs to be more sensitive to discriminate between genuine and forge person. Among the different identification methods, the common method used in our society is to identify a person is through handwritten signature because it is an official/formal way for person identification. They are used in government, for attestation, documents authenticity etc.
Vos, T. E., Mar´ın, B. & Escalona, M. J., 2012. A Methodological Framework for Evaluating Software Testing Techniques and Tools. International Conference on Quality Software, 3(2), pp. 22-34.
However, with the advent of sophisticated devices and powerful computers, an effort to automate the process has been noted in the recent past. Unfortunately, the proposed methods were not fully capable of addressing the challenges in automatic crack detection. The main difficulties encountered in automatic crack detection methods are variable lighting conditions, random camera/view angles, and random resolution of bridge images. Moreover, we found that automatic crack detection gets even harder where the background texture randomly changes and hence segmentation of background and foreground elements becomes very challenging. This article proposes a non-trivial and novel method which addresses the above mentioned challenges efficiently.
Rayne, PB, Kulkarni, P, Patil, S & Meshram, BB 2012, ‘Authentication and Authorization:Tool for Ecommerce Security’, Engineering Science and Technology: An International Journal, vol. 2, no. 1, pp. 150-157. Schneider, GP 2011, Electronic commerce, ninth edition, Joe Sabatino, Boston.
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2012. Jamil, Danish, and Hassan Zaki. "Security Issues in Cloud Computing and Countermeasures." International Journal of Computer Science and Engineering 3.4 (2011): 2672-676. Academic Search Complete.
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2, February 2013  Jun Wang, Sanjiv Kumar and Shih-Fu Chang” Semi- Supervised Hashing for Large Scale Search” IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 34, No. 12, December 2012  Chenxia Wu, Jianke Zhu, Deng Cai, Member, Chun Chen, and Jiajun Bu, “Semi-Supervised Nonlinear Hashing Using Bootstrap Sequential Projection Learning” IEEE Transactions On Knowledge And Data Engineering, Vol. 25, No. 6, June 2013  Archana B. Waghmare” Low-Level Feature Extraction for Content-Based Image Retrieval” International Journal of Advances in Computing and Information Researches ISSN: 2277- 4068, Volume 1– No.2, April 2012  Dr. H. B. Kekre, Mr.Dhirendra Mishra” Image Retrieval using Image Hashing” Techno-Path: Journal of Science, Engineering & Technology Management, Vol.