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Introduction Information Description Functional Description Behavioral Description
Applications Validation Criterion Conclusion
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Conclusion
Reliable personal recognition is critical to many business processes. Biometrics refers to automatic recognition of an individual based on her behavioral and/or physiological characteristics. The conventional knowledge-based and token-based methods do not really provide positive personal recognition because they rely on surrogate representations of the person’s identity (e.g., exclusive knowledge or possession). It is thus obvious that any system assuring reliable personal recognition must necessarily involve a biometric component. This is not, however, to state that biometrics alone can deliver reliable personal recognition component. In fact, a sound system design will often entail incorporation of many biometric and non-biometric components (building blocks) to provide reliable personal recognition.
Automated Personal Identification System using Fingerprint recognition also has some limitations that may have adverse implications for the security of a system. While some of the limitations of biometrics can be overcome with the evolution of biometric technology and a careful system design, it is important to understand that foolproof personal recognition systems simply do not exist and perhaps, never will. Security is a risk management strategy that identifies, controls, eliminates, or minimizes uncertain events that may adversely affect system resources and information assets. The security level of a system depends on the requirements (threat model) of an application and the cost-benefit analysis. In our opinion, properly implemented biometric systems are effective deterrents to perpetrators. There are a number of privacy concerns raised about the use of biometrics. A sound trade-off between security and privacy may be necessary; collective accountability/acceptability standards can only be enforced through common legislation.
We have proposed the attendance system in Universities and schools through fingerprint recognition. It’s a fact that roll calls savor a considerable amount of time. So if an embedded system is designed which has its internal memory and a counter to take care of the time assigned for each period along with a embedded program which handles the daily schedule, and not to forget the maths co-processor which manages the serious mathematical connotations associated with the processing of the fingerprint image, then this handheld device could be easily used for daily attendance, by passing it on through the entire class, or keeping it at the entrance of the class. The concerned teacher can either connect the device at the end of the day, and upload the useful information about a student’s presence into the database, or maybe the device can be connected with the database online so that the attendance is updated instantaneously. This may increase the efficiency of the system as well make the attendance system transparent to all.
As biometric technology matures, there will be an increasing interaction among the market, technology, and the applications. This interaction will be influenced by the added value of the technology, user acceptance, and the credibility of the service provider. It is too early to predict where and how biometric technology would evolve and get embedded in which applications. But it is certain that biometric-based recognition will have a profound influence on the way we conduct our daily business.
Bibliography
1. Filterbank-Based Fingerprint Matching
Anil K. Jain, Fellow, IEEE, Salil Prabhakar, Lin Hong, and Sharath Pankanti
2. Fingerprint Image Enhancement: Algorithm and Performance evaluation
Lin Hong, Student Member, IEEE, Yifei Wan, and Anil Jain, Fellow, IEEE
3. Fingerprint Enhancement in the Singular Point Area
Sen Wang, Student Member, IEEE, and Yangsheng Wang]
4. Minutiae Detection Algorithm for Fingerprint Recognition
Virginia Epinoso – Duro, Polytechnic University of Catalonia.
5. Feature Extraction—A Pattern for Information Retrieval.
Dragos¸-Anton Manolescu
6. A hybrid fingerprint matcher .Arun Rossa, Anil Jain, James Reisman
7. Digital Image Processing: Concepts, Algorithms & Scientific Applications
By Bernd Jahne
8. Image Processing In C By Dwayne Philips
9. Digital Image Processing Techniques By Anil Kumar Jain
10. Feature Extraction Using a Chaincoded Contour ,Representation of
Fingerprint Images
Venu Govindaraju, Zhixin Shi
CEDAR, Department of Computer Science and Engineering,
John Schneider
11. Software Engineering – A practitioners approach – Roger Pressman
12. IEEE Online journals
13. www.google.com