Fingerprint Recognition System
Abstract
Fingerprint recognition system consist of image preprocessing, features extraction and features matching that runs effectively and accurately on personal computer. The image preprocessing includes noise removal, histogram equalization, global thresholding and ridgeline thinning which are necessary for the features extraction. Extracted features are then stored in a file for fingerprint matching. Matching algorithm presented here is a simple, fast and accurate. Experimental results for matching are accurate, reliable and fast for implementation using a personal computer and fingerprint reader. The proposed fingerprint algorithm can provide an effective way of automated identification and can be extended to other security or identification applications. Further the algorithm can be implanted on a FPGA platform for a real time personal automated identification system.
Keywords:biometric recognition;histogram equalization;ridge thinning;ridge ending;ridge bifurcation.
1. INTRODUCTION
Fingerprint recognition systems are termed under the umbrella of biometrics. Biometric recognition refers to the distinctive physiological (e.g. fingerprint, face, iris, retina) and behavioral
(e.g. signature, gait) characteristics, called biometric identifiers or simply biometrics, for automatically recognizing individuals. In 1893, it was discovered that no two individuals have same fingerprints. After this discovery fingerprints were used in criminal identification and till now fingerprints are extensively used in various identification applications in various fields of life. Fingerprints are graphical flow-like ridges present on human fingers. They are fully formed at about seventh month of fetus development and fingerprint configuration do not change throughout
the life except due to accidents such as bruises or cut on fingertips.
Because of immutability and uniqueness, the use of fingerprints for identification has alway
s been of great interest to pattern recognition researchers and law enforcement agencies. Conventionally, fingerprint recognition has been conducted via either statistical or syntactic approaches. In statistical approach a fingerprint’s features are extracted and stored in an n-dimensional feature vector and decision making process is determined by some similarity measures. In syntactic approach, a pattern is represented as a string, tree [1], or graph [2] of fingerprint features or pattern primitives and their relations. The decision making process is then simply a syntax analysis or parsing process.
This paper suggests the statistical approach. Experimental results prove the effectiveness of this method on a computer platform, hence making it suitable for security applications with a relatively small database. The preprocessing of fingerprints is carried out using modified basic filtering methods which are substantially good enough for the purpose of our applications with reasonable computational time. Block diagram for the complete process is shown in Figure.1.
2. IMAGE PREPROCESSING
For the proper and true extraction of minutiae, image quality is improved and image preprocessing is necessary for the features extraction because we cannot extract the required points from the original image. First of all, any sort of noise present in the image is removed. Order statistics filters are used to remove the type of noise which occurs normally at image acquisition. Afterwards the following image preprocessing techniques are applied to enhance the fingerprint images for matching.
2.1 Histogram Equalization
This method is used where the unwanted part of the image is made lighter in intensity so as to
emphasize the desired the desired part. Figure 2(a) shows the original image and Figure 2(b) histogram equalization in which the discontinuities in the small areas are removed. For the histogram equalization, let the input and the output level for an arbitrary pixel be i and l, respectively. Then the accumulation of histogram from 0 to i ( 0 ≤ i ≤ 255,0 ≤ k ≤ 255) is given by
where H(k) is the number of pixel with gray level k, i.e. histogram of an area, and C(i) is also
known as cumulative frequency.
     
2.2 Dynamic Thresholding
Basic purpose of thresholding is to extract the required object form the background. Thresholding is simply the mapping of all data points having gray level more that average gray level. The results of thresholding are shown in Figure 3.
2.3 Ridgeline Thinning
Before the features can be extracted, the fingerprints have to be thinned or skeletonised so that all ridges are one pixel thick. When a pixel is decided as a boundary pixel, it is deleted directly form the image [3-5] or flagged and not deleted until the entire image been scanned [6-7]. There are deficiencies in both cases. In the former, deletion of each boundary pixel will change the object in the image and hence affect the object symmetrically. To overcome this problem, some thinning algorithms use several passes in one thinning iteration. Each pass is an operation to remove boundary pixels from a given direction. Pavlidis [8] and Fiegin and Ben-Yosef [9] have developed effective algorithms using this method. However, both the time complexity and memory requirement will increase. In the latter, as the pixels are only flagged, the state of the bitmap at the end of the last iteration will used when deciding which pixel to delete. However, if this flag map is not used to decide whether a current pixel is to be deleted, the information generated from processing the previous pixels in current iteration will be lost. In certain situations the final skeleton may be badly distorted. For example, a line with two pixels may be completely del
eted. Recently, Zhou, Quek and Ng [10] have proposed an algorithm that solves the problem described earlier and is found to perform satisfactorily while providing a reasonable computational time. The thinning effect is illustrated in Figure 4

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