FINGERPRINT RECOGNITIONALGORITHM

OUTLINE ……

Objective

What is Fingerprint?

What is Fingerprint recognition>

Algorithm for Fingerprint Recognition.

Preprocessing Stages.

Minutia Extraction.

Minutia Match.

Result And Discussion

Conclusion

Objective

• The objective is to implement finger print recognition algorithm by using Minutia Extraction and Minutia Matching.

• The Region of Interest (ROI) for each fingerprint image is extracted after enhancing its quality.

• That is used to extract the minutia, followed by minutiae extraction.

• Application :

• Data Security

• Crime Investigation

• Security Lock

What Is Fingerprint?

• Skin on human fingertips contains ridges and valleys which together forms distinctive patterns. These patterns are called FINGERPRINTS.

• However, shown by intensive research on fingerprint recognition, fingerprints are not distinguished by their ridges and furrows, but by features called Minutia, which are some abnormal points on the ridges

• Among the variety Of minutia types reported in literatures, two are mostly significant and in heavy usage:

I. Ridge ending- the abrupt end Of a ridge .

2. Ridge bifurcation- a single ridge that divides into two ridges .

What Is Fingerprint Recognition?

• Fingerprint recognition is the process of comparing questioned and known fingerprint against another fingerprint to determine if the impressions are from the same finger or palm.

• It includes two sub-domains: one is fingerprint verification and the other is fingerprint identification.

Algorithms For Fingerprint Recognition

Minutia Extraction........

Ridge Thinning:

• Ridge Thinning is to eliminate the redundant pixels of ridge still the ridges are just one pixel wide.

• An iterative, parallel thinning algorithm is used for ridge thinning.

Minutia Marking:

• After the fingerprint ridge thinning, marking minutia points is relatively easy.

• The concept of Crossing Number (CN) is widely used for extracting the minutia.

Minutia Matching........

• Minutia match algorithm determines whether the two minutiae sets are from same finger or not. It include two stages:

— Alignment Stage

— Match Stage

• Alignment stage:- Given two fingerprint images to be matched, any one minutia from each image is chosen, and the similarity of the two ridges associated with the two referenced minutia points is calculated

• Match stage: After obtaining two set of transformed minutia points, the elastic match algorithm is used to count the matched minutia pairs by assuming two minutia having nearly the same position and direction are identical.

Result And Experiment

Minutia Marking:

RESULT AND DISCUSSION

• Minutia Matching:

Here we had taken two different sets of fingerprints.

• 1.Two different angles of a same fingerprint

• 2. Fingerprints of two different finger.

Using Match Score we distinguish two fingerprints are same or not.

RESULT AND DISCUSSION

•The match score value between the two images is 0.67 .

The value is greater or same as threshold value.

•We conclude that these two fingerprints are of same person.

RESULT AND DISCUSSION

•The match score value between the two images is 0.37.

• This value less than threshold value.

•We conclude that these two fingerprints two persons.

Conclusion

• The above implementation was an effort to understand how Fingerprint Recognition is used as a form of biomeüic to recognize identities of human beings.

• It includes all the stages from enhancement to minutiae extraction of fingerprints.

• There are various standard techniques are used in the intermediate stages of processing.

• At last minutiae extraction and comparison happens.