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This paper proposes thumb impression recognition and matching system to implement an authenticated ration card. Ration card is essential for each and every family. It provides identification proof for all the individuals in the country. In this paper, we are going to design smart ration card. By swiping the smart ration card in the card reader, the commodities which we buy gets automatically registered in our account and the processor stores all the details. It also uses thumb impression of the head of the family which serves as the unique identity for each card holder. This method avoids faulty calculation and gives protection t o every individual in the country. The proposed smart ration card is being simulated using MATLAB and implemented in ARUDINO board. The goal of this idea is to propose secured smart ration card to avoid faulty calculation and fraud in the nation.
Public Distribution System (PDS) is actually an Indian foodstuff protection system . This can be recognized because of the government connected with consumer affairs, food, along with public distribution and managed jointly with state governments inside India. The traditional PDS is meant to distribute grocery products to India’s citizen . The current validity as well as the allocation of the ration card is usually monitored from the state governments. Making this process computerized is usually meant to remove ones drawback of the offer technique of issuing goods based on ration card. Ones main drawback at the current system can be that the PDS have been criticised for its urban bias as well as its failure to be able to serve your weaker economic sections of a population effectively. All times users do not get the rightful entitlement regarding quantity. So in order to avoid most these types of drawbacks we are going for smart ration card which will help us to avoid corruption .
The fingerprint database of the head of the family is stored in the ration card and the dealer of the ration supplier. By the matching of both databases, the items in the ration will be supplied according to the monthly updates.
2. Overview of fingerprint patterns:
2.1 Finger Print:
A fingerprint with the narrow sense can be the impression left by the friction ridges of the human finger. The finger print of individual is unique and remains unchanged greater than the lifetime. The finger print can be formed from an impression of a pattern connected with ridges on a finger. The probability that two finger prints are alike is about 1 in 1.9*10 15.
2.2 Different Patterns:
A finger print has several patterns. Several patterns include arch, whorl, and loop. In whor l pattern the core point is present in the middle region . In loop, the core point lies in the top region of the innermost loop. Arch is the semi -circle.
2.3 Recognition and Matching:
Fingerprint recognition is a method of identifying the match between the two finger prints. Matching can be done by fingerprint patterns based on ridges, arch, loop, and whorl. Matching a input visual which has a held template involves computing the current variety of the squared differences between the two feature vectors soon after discarding missing values. This distance is actually and the distance is computed. The matching score will be combined with the finger print obtained from the minutiae-based method. The finger print recognition system compares the two fingerprints, and the matching score is computed for determining successful matching of the given input finger print template.
3. Proposed methodology for fingerprint recognition and classification:
The proposed simulation of fingerprint recognition and classification is shown in figure 2. Finger print recognition consists of the following stages:
1. Image pre-processing: Image enhancement
2. Minutiae extraction: Thinning, Detecting core points
3. Post-processing: Database matching
Extracting a finger print image by using finger print scanner . Finger Print image is extracted and then that fingerprint image is compared with the template. To obtain the fingerprint image, the finger is then shifted just vertically, just horizontally, half the window size and the grid scan is completed. Therefore, it takes four scans of the image to do the linear scan .
Matching is done in two different ways, they are correlation based matching and minutiae matching. Matching help us to compare the current finger print and the template.
3.2.1 Correlation Based Matching:
This is the process where two finger print images are superimposed and correlation between corresponding pixels is computed for different alignments.
3.2.2 Minutiae Matching:
Minutiae could be the small actual particulars at the image. In this technique regarding matching, minutiae are extracted via two fingerprints and stored like a set of easy steps in 2 -D planes. It consists of finding the alignment between template and also the input minutiae sets that result on the maximum amount associated with minutiae pairings.
3.3 Performance measure of scanned fingerprint:
The performance of the matching of two finger print is computed by using matching ratio.
The matching ratio can be done by the ratio of number of matched minutiae to the maximum number of minutiae from both the fingerprint images. By this formula the matching ratio can be determined. It can be found between two or more images