Review on Fingerprint Recognition Techniques

Volume 2, Spl. Issue 2 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Review on Fingerprint Recognition Techniques 1,2

Nisha Gupta1, Anshul Kumar2 Baddi University of Emerging Sciences& Technology,Baddi, H.P.

1

nishaguptaec006@gmail.com, 2anshul.kumar@baddiuniv.ac.in

Abstract: Fingerprints have been used for over a century and are the most widely used form of biometric identification.

Fingerprint identification is commonly employed in forensic science to support criminal investigations, and in biometric systems such as civilian and commercial identification devices. Various techniques are proposed by various authors for fingerprint recognition.This paper reviews the fingerprint recognition techniques proposed by different authors.

Minutiae are major features of a fingerprint, using which comparisons of one print with another can be made. Some of the fingerprint features are shown in fig.2

Keywords-Fingerprint recognition, Minutiae matching, Biometric authentication, Phase based matching, Feature based matching.

I. INTRODUCTION The uniqueness and permanence of the fingerprints are very well-know. Archaeological artifacts prove that fingerprints were already used by the ancient Assyrians and Chinese as a form of identification of a person. The first scientific studies on fingerprints date from the late sixteen century, but the fundamentals of modern fingerprint identification methods were provided at the end of nineteenth century. The uniqueness of fingers is used in many forensic cases to solve crimes, as some of the most common evidence found at a crime scene is fingerprints.Fingerprint recognition [1][2] is a rapidly evolving technology that has been widely used in forensics such as criminal recognition and prison security, and has a very strong potential to be widely adopted in a broad range of civilian applicationsthe verification usually relies exclusively on minutiae features. Minutiae are local feature marked by ridge discontinuities.Minutiae points are local ridgecharacteristics that occur at either a ridge bifurcation or a ridge ending. A ridge endingoccurs when the ridge flow abruptly terminates and a ridge bifurcationis marked by a fork in the ridge flow.

Figure 2: A Fingerprint Feature

II.RELATED WORK In the field of fingerprint recognition,different works have been done so far.Manisha Redhu and Dr.Balkishan performed fingerprint Recognition Using Minutiae Extractor [3].The authors proposed the concept of Minutiae based matching technique. In this technique the minutiae are extracted from the two fingerprint images and stored as sets of points in the two-dimensional plane.

The authors performed the technique which includes minutiae extraction and minutiae matching. The detailed design description of the proposed technique is explained in fig.3.

Image

Image Enhancement and Segmentation

Final Extraction

Minutiae Alignment Fig 1: A Fingerprint Image

Match Fig 3: Detailed Design Description

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Volume 2, Spl. Issue 2 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

The first stage in the minutiae extraction is the Image enhancement stage. Image enhancement improves the image quality. The authors implemented three enhancement techniques: Histogram Equalization[4], Fast Fourier Transformation [5] and Image Binarization [6].

Histogram is a process that attempts to spread out the gray levels in an image so that they are evenly distributed across their range. This method usually increases the global contrast of many images, especially when the usable data of the image is represented by close contrast values. The Fast Fourier transform is done to find the frequency of the pixel .So that the output would be an image in the frequency domain. Image Binarization is done to convert a 256-level image to a 2-level image It differentiates image pixels from background because of variations in contrast, locally adaptive thresholding is used.

The next stage in the minutiae extraction is the final extraction processes. This stage involves Ridge Thinning, Minutiae Marking, False Minutiae Removal and Minutiae Representation. The last stage in the minutiae extraction is the minutiae matcher.After successfully extracting the set of minutia points of two fingerprint images to be tested, the authors perform Minutiae Matching algorithm to check whether the two fingerprints match or not. Minutiae matcher includes Minutiae alignment and match process. The authors proposed the match ratio which is given as Match Score = {Num(Matched Minutia)}

III.CONCLUSION As fingerprint technology matures, there will be increasing interaction among market, technology, and applications. The emerging interaction is expected to be influenced by the added value of the technology, the sensitivities of the user population, and the credibility of the service provider. There is need of efficientmethod for fingerprint recognition which willreduce computational time and increase efficiency.

(1)

{Max(Num Minutia(image1,image2))} The match score ranges from 0 to 100. If the match score is larger than a pre-specified threshold, then the two fingerprints are from the same finger. Koichi Ito, Ayumi Morita, Takafumi Aoki, Hiroshi Nakajima,Koji Kobayashi, and Tatsuo Higuchi [7]purposed fingerprint recognition algorithm which is a combination of phase based matchingtechnique and Feature based matchingtechnique[]. In phase based matching process Phase-Only Correlation (POC) function[8]is used. Lettwo imagesN1xN2..AlsoletF(k1, k2)and G(k1, k2)denote the 2D Discrete Fourier Transforms of the two images.

F(k1, k2) =∑ f(n1, n2)Wk1n1N1Wk2n2N2 (2) = AF(k1, k2)ejθF(k1,k2), When two images are same, their POC function gives a distinct sharp peak. When two images are not same the peak drops.The height of the peak gives the similaritymeasure for image matching, and the location of the peak shows the translationaldisplacement between the two images. In this fingerprint recognition technique, the authors use the Band-LimitedPhase-Only Correlation (BLPOC) which is the modified form of POC.The fingerprint matching algorithm using BLPOC function consists of (i) rotation and displacement alignment(ii) common region extraction and (iii) matching score calculation withprecise rotation.The authors proposed the feature based matching algorithm which consists of (i) minutiae extraction, (ii) minutiaepair correspondence, (iii) local block matching using BLPOC function, and(iv) matching score calculation. The overall proposed algorithm consists of rule-based fingerprint classification

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method, phased based matching method and feature based matching method. The authors divides the fingerprints into 7 categories:“Arch”, “Left Loop”, “Right Loop”, “Left Loop or Right Loop”, “Arch or Left Loop”, “Arch or Right Loop”, and “Others”. If the two fingerprints fall into different categories, it gives the overall score S = 0, otherwise matching operation is performed to evaluate the overall score. The feature matching stage evaluates the matching score SF. The phase matching stage evaluates the matching score SP. The overall matching score S is the combination of SFand SPand is given by S = α × SF+ (1 − α) × SP (3) The authors performed the proposed algorithm and minutiae matching algorithm by using FVC 2002 DB1 set A [] and showed that Equal Error Rate (EER) for proposed algorithm is 0.78% and for minutiae based algorithm is 1.82%.

REFERENCES [1]. D. Maio, D. Maltoni, A. K. Jain, and S. Prabhakar. Handbook of Fingerprint Recognition. Springer Verlag, 2003.

[2].Wayman, J., Jain, A., Maltoni, D., Maio, D.: Biometric Systems.

Springer (2005) [3]. Manisha Redhu , Dr.Balkishan: Fingerprint Recognition Using Minutiae Extractar in International Journal of Engineering Research and Applications(2013) [4]. Yun, Se-Hwan, Jin Heon Kim, and Suki Kim, Image enhancement using a fusion framework of histogram equalization and laplacian pyramid. Consumer Electronics, IEEE Transactions on 56, no. 4 2763-277, 2010.

[5]. Mao, Keming, Zhiliang Zhu, and Huiyan Jiang, A fast fingerprint image enhancementmethod in Computational Science and Optimization (CSO), 2010 Third International Joint Conference on, vol. 1, pp. 222-226. IEEE, 2010.

[6]. L.C. Jain, U.Halici, I. Hayashi, S.B. Lee and S.Tsutsui. Intelligent biometric techniques in fingerprint and face recognition, the CRC Press, 1999.

[7].Koichi Ito, Ayumi Morita, Takafumi Aoki, Hiroshi Nakajima,Koji Kobayashi, and Tatsuo Higuchi:A Fingerprint Recognition Algorithm Combining Phase-Based Image Matching and Feature-Based Matching inSpringer-Verlag Berlin Heidelberg 2005 [8]. Kuglin, C.D., Hines, D.C.: The phase correlation image alignment method. Proc.Int. Conf. on Cybernetics and Society (1975) 163–165.

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