Multi-Biometrics Recognition Systems for Intelligent House Management

This final year project describes the design and development of applying the biometrics technology with facial recognition and fingerprint recognition system. Several commonly used applications for our biometric system are reviewed in this report. Details of the design criteria of both hardware and software principles of operation as well as the testing and verification of the system are presented. Further development of project is also proposed and recommended. This project is to develop and built up a biometrics system for intelligent house and clubhouse. As biometrics is a popular and safety technology, the project applies this technology to improve the security level and easy for management.

How does it work?

False Reject Rates:

For most applications, letting the good guys in is just as important as keeping the bad guys out . The probability that a biometric device won't recognize a good guy is called the "False Reject Rate." The False Reject Rates quoted for current biometric systems range from 0.00066% to 1.0%. A low False Reject Rate is very important for most applications, since users will become extremely frustrated if they're denied access by a device that has previously recognized them.


There has a example may be helpful.

A company with 100 employees has a biometric device at its front door. Each employee uses the door four times a day, yielding 400 transactions per day. A False Reject Rate of 1.0% predicts that every day, four good guys (1% of 400) will be denied access. Over a five-day week, that means 20 problems. Reducing the False Reject Rate to 0.1% results in just two problems per week. A low False Reject Rate is very important for most applications, since users will become extremely frustrated if they're denied access by a device that has previously recognized them. As mentioned previously, the combination of a low False Reject Rate plus a simple keypad code provides virtually unbreakable security.

Equal Error Rates:

Error curves give a graphical representation of a biometric device's "personality." The point where false accept and false reject curves cross is called the "Equal Error Rate." The Equal Error Rate provides a good indicator of the unit's performance. The smaller the Equal Error Rate, the better.

Validity of Test Data:

Testing biometrics is difficult, because of the extremely low error rates involved. To attain any confidence in the statistical results, thousands of transactions must be examined. Some error rates cited by manufacturers are based on theoretical calculations. Other rates are obtained from actual field testing. Field data are usually more reliable. In the case of False Reject rates, only field test data can be considered accurate, since biometric devices require human interaction. For example, if the device is hard to use, false reject rates will tend to rise. A change in the user's biometric profile could also cause a false reject (a finger is missing, for example). None of these conditions can be accurately quantified by purely theoretical calculations. On the other hand, False Accept Rates can be calculated with reasonable accuracy from cross-comparison of templates in large template databases.

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  • Rajdeep Janorkar

    Multi-Biometrics Recognition Systems for Intelligent House Management 1 month ago