A fingerprint recognizing system is built with two principal components: the fingerprint administrator and the fingerprint recognizer. Fingerprints are identified by their special features such as ridge endings, ridge bifurcation, short ridges, and ridge enclosures, which are collectively called the minutiae. It explains the finger print characteristics that are used to identify individuals and the process of minutiae extraction. The fingerprint administrator uses the method of gray scale ridge tracing backed up by a validating procedure to extract the minutiae of fingerprints. The fingerprint recognizer employs the technique of fuzzy evolutionary programming to match the minutiae of an input fingerprint with those from a database.
Introduction:
Fingerprints of an individual are unique and are normally unchanged during the whole life. This method has been widely used in criminal identification, access authority verification, financial transferring confirmation, and many other civilian applications. In the old days, fingerprint recognition was done manually by professional experts. But this task has become more difficult and time consuming In this paper, we explain the method of direct gray scale minutiae detection proposed in improved by a backup validating procedure to eliminate false minutiae. As for minutiae matching, we employed the technique of fuzzy evolutionary programming, which has been used successfully in speaker identification, images clustering, and fuzzy algebraic operations.Finger Print Characteristics:
A fingerprint is a textural image containing a large number of ridges that form groups of almost parallel curves (Figure 1). It has been established that fingerprint's ridges are individually unique and
are unlikely to change during the whole life.
Although the structure of ridges in a fingerprint is fairly complex, it is well known that a fingerprint can be identified by its special features such as:
Ridge endings: The ending of the ridges takes place at the middle as shown in fig 2(a) .
Ridge bifurcation: The division of the ridges in the middle as shown in fig 2(b).
Short ridges: The small lines present in between two ridges as shown in fig2(c). and
Ridge enclosures: These are the loops formed between the ridges as shown in fig 2(d).
These ridge features are collectively called the minutiae of the fingerprint. A full fingerprint normally contains 50 to 80 minutiae. According to the Federal Bureau of Investigation, it suffices to identify a fingerprint by matching 12 minutiae.
Minutae Extraction:
For convenience, we represent a fingerprint image in reverse gray scale. That is, the dark pixels of the ridges are assigned high values where as the light pixels of the valleys are given low values. Figure 3 shows a section of ridges in this representation.
In a fingerprint, each minutia is represented by its location (x, y) and the local ridge direction Figure 4 shows the attributes of a fingerprint's minutia. The process of minutiae detection starts with finding a summit point on a ridge, and then continues by tracing the ridge until a minutia, which can be either a ridge ending or bifurcation, is encountered.
Finger Print Recongnition:
The primary purpose of our fingerprint recognizing system is to calculate the matching degree of the target fingerprint with the images in a database and to decide if it belongs to a particular individual. A fingerprint is said to match one image in the database if the degree of matching between its minutiae and that of the image in the database is higher than some prespecified acceptance level. The method of calculating this matching degree is based on our fuzzy evolutionary programming technique.
Conclusion:
We have pre nizing system that uses the method of gray scale ridge tracing backed up by a validating procedure to detect fingerprint's minutiae and that employs the technique of fuzzy evolutionary programming to match two sets of minutiae in order to identify a fingerprint. The experimental results show that the system is highly effective with relatively clean fingerprints. However, for poorly linked and badly damaged fingerprints, the system appears to be not so successful.
In order to handle those bad types of fingerprints, the addition of a preprocessing component that also adopts the fuzzy evolutionary approach to reconstruct and enhance the fingerprints before they are processed by the system. Also, it is possible to connect the system with a live fingerprint scanner that obtains a person's fingerprint directly and sends it to the system for identification.
References:
- Arcelli, C., and Baja, G.S.D "A Width Independent Fast Thinning Algorithm", IEEE Trans. Pattern Analysis Machine
- Baruch, O."Line Thinning by Line Following", Pattern Recognition Letters, Vol. 8, No. 4, 1988, pp. 271-276.
Technical Paper on Finger Print Recognizer using Fuzzy Evolutionary Programming
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