Chapter user. Systems FAR are indicate the ratio value

Chapter Nine: Conclusion and Future Work // done

9.1: Evaluation of the system:

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The reliability of the
fingerprint image systems are precision relies that obtain in the minutiae
extraction process, however the main factor that can damaging the corrected
location of minutiae is poor quality for image; this factor was solved when we
used Alignment- based match.  

There are many type for
performance evolution indexes, which use for determining performance of fingerprint
recognition systems such that:


9.1.1:False Rejection Rate:  

As we say in previous sections;
the biometric security system maybe rejected correct attempt access by
authorized user; so the measure “FAR” is used in this type, the following
formula is basically used to enhance the FFR.

In general, FRR  ” Incorrect Rejected ” is the  measure of possible of biometric system to
classify  any authorized user as invalid

Therefore, FRR is basically state
the ratio value between the number of False Rejection and number of
identification attempt.

9.1.2: False Acceptance Rate:

FAR happened when the security of
biometric systems are accepted the attempt of the unauthorized user. Systems
FAR are indicate the ratio value between number of false acceptance and number
of identification attempt.

     FAR(%) = (FA/n)*100 ;


     FA= false accepted , n =sample number.

Another Formula :

is  basically  define when the system obtain unauthorized
access  as a valid user.                    

When  we say the biometric system become more
security that’s mean the system is provide low False Accepted Rate.



9.2: Conclusion:

When we backed many years ago, we
found many technique used to verification the fingerprint such as:

Traditional technique: It the widest technique, it worked by taking
impression of hand’s used ink then pressing 
covered finger on to the paper to obtain fingerprint.

Those used technique based
on minutiae matching technique ,ARPS  and Pattern-based matching, which are
relatively fast and less expensive, improves the matching speed, improve
feature extraction and accuracy more 
than other algorithms.

After system analysis, Euclidean matching templates algorithm  is the best, since it’s computed matching rate
in less time and easy way. All future work based on PCA recognition tried
to achieve more accuracy and performance .