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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 6, June 2016

Minutiae and Texture Based Altered Fingerprint Using STFT Analysis 1

C. Celestine Nebia, 2J.P. Jayan, 3Dr. M.K. Jeyakumar, Research Scholar in Computer Science, Noorul Islam Centre for Higher Education, Kumaracoil. 2 Research Scholar in Computer Science, Bharathiyar University, Coimbatore. 3 Professor in Computer Applications, Noorul Islam Centre for Higher Education, Kumaracoil.

Abstract: Developing an automatic solution to detect altered fingerprints is the first step in defeating fingerprint alteration. Fingerprint quality assessment routines used in most fingerprint identification systems, such as the open source NFIQ (NIST Fingerprint Image Quality) software may be useful in detecting altered fingerprints if the corresponding images are indeed of poor quality. But, not all altered fingerprint images have poor quality. Since existing fingerprint quality assessment algorithms are designed to examine if an image contains sufficient information for matching, they have limited capability in determining if an image is a natural fingerprint or an altered fingerprint. Improve the quality of image (fingerprint) from the database and convert them into high quality fingerprint. Then the process is evaluating the algorithm. This result shows the detecting altered fingerprint. Keywords: Fingerprints, AFIS, Ridge Patterns, Image Quality, Minutiae, Distribution I. INTRODUCTION Fingerprints are the scraps of an recession outlander the contention ridges of human in the street addition of a fleshly or other primate cancel. A contour bottom except for surrender an downheartedness of discord ridges. Contention insigne is subside as a lifted up quota of the outer layer on the digits (Fingers and toes), the lay off of the hand or the well-designed of the currish, and it consists of combine or with regard to joined reach appropriate of friction seal superficial, sometimes known as epidermal ridges. These ridges may apart from instigate in appealing estimated surfaces and may improve surface contact in wet conditions. Fingerprint impressions may be left behind on a surface by the normal secretions of disconcert distance newcomer disabuse of the eccrine glands [1][17], go off are realized in conflict seal exotic or they may be in debt by ink or adjustment substances transferred stranger the peaks of opposition ridges on the external to a on the whole cool come up such as a fingerprint card. As a service to the ancient 20th century, Fingerprint analysis and detection has been unite of the foremost qualified ancient and import styles of crime scene rhetorical investigations. Shortly exotic crimes go been resolved encompassing watch proof that for the

other reason. Fingerprint distinction, is the activity of comparing one time of discord pinnacle surface despair (see details), from base fingers or hooves or equanimity the push off of the give away or one of the foot and determining whether these impressions are from the same individual known as dactyloscopy. Finger points: In fingerprinting term, finger points are the point of interest in a fingerprint, Such as bifurcations (a ridge splitting in to two) and ridge ending as shown in fig 1.1.

Fig 1.1: Finger points

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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 6, June 2016

There aren't any clear-cut boundaries within the time from image process at one finish to pc vision at the opposite. However, a helpful paradigm is to think about 3 styles of computerised processes during this continuum: low, mid, and high-level processes. Low-level processes involve primitive operations, like image preprocessing to cut back noise, distinction sweetening, and image sharpening. A lowlevel method is characterised by the actual fact that each its inputs and outputs generally are pictures. Mid-level processes [2] on pictures involve tasks like partitioning a picture into regions or objects, description of these objects to cut back them to a type appropriate for pc processing, and classification (recognition) of individual objects. A midlevel method is characterised by the actual fact that its inputs typically pictures, however its outputs are attributes extracted from those pictures (e.g., edges, contours, and also the identity of individual objects). 1.1Altered Fingerprints Finger prints are the most dependable shape of identity. Fingerprints are constantly particular and permanent. They are formed during fetal growth and while the man or woman grows the fingerprints gets bigger, then they remained unchanged till death, except barring injury. For over a century law enforcement and border control were using this fingerprint to discover the criminals from the latent impressions they have got left in the back of at crime scenes. Of direction the most effective manner for the criminals is to wear gloves in order to avoid leaving identifiable finger prints. But a few criminals have taken an extreme measure with the aid of bodily deforming their prints. The extreme measures taken by the criminals are making surgery, burning, or applying acid to destroy the papillary ridges, so that the law enforcement can’t identify the criminal by their fingerprints [2][3]. Some have even used plastic surgeons as shown in figs 1.2 (a), (b), (c) and (d).

Fig: 1.2 (a) Transplanted from foot (b) Bitten (c) Burnt with acid (d) Surgically altered

In the long run, these efforts are typically inadequate because this harm is in and of itself, specific but the burns or cuttings transect the friction ridge pores and skin is as particular because the natural ending ridges and

bifurcation examiners usually rely upon to make identifications. As you consider, this conduct is uncommon so finding a suspect with altered fingerprints is relatively suggestive that they're involved in criminal interest. One of the quantity one makes use of distorted fingerprints is to mask one’s individual characteristics [17]. Observe that altered fingerprints square measure exclusive from pretend fingerprints. Fake palms is made up of glue, latex, or polymer, and it's a properly publicised methodology to bypass fingerprint systems. Altered fingerprints square measure real hands which might be accustomed conceal one’s own identity in order that you'll be able to keep one's distance from their identity with the help of a biometric system, when an individual use pretend hands solely to undertake each different identification. Altered fingerprints falls at a lower place a broader class of assaults stated as biometric obfuscation. Obfuscation (unclear image) is represented as a planned attempt by employing a person to mask his identity from a biometric device. The problem of altered fingerprint footage has overinvolved analysis on this space. One in every of the first goal of this studies is to spotlight the importance of the difficulty, learning altered fingerprints automatic detection and matching algorithmic rule for them growing an automatic strategy to find altered fingerprints is the first step in defeating fingerprint alteration. A collection of rules is developed to find altered finger prints habitually based mostly at the characteristics of the fingerprint orientation space and finger points distribution. The difficulty of fingerprint alteration or unclear image has received very little or no interest. Fingerprint unsure photograph refers back to the deliberate alteration of the fingerprint pattern via a personal for the aim of overlaying his identity. To boot NFIQ algorithmic rule wont to assess the large info of altered fingerprints. During this paintings, we planned matching set of rules to find and rectify pores and skin distortion based mostly wholly on a divorced fingerprint photograph to resolve the regression downside set of rules to look at the fingerprint. The aim of this thesis is to point out the practicableness of the planned matching algorithmic rule approach in detective work altered fingerprints. The automatic detector must satisfy the following three requirements: 1. Given the big turnout demand of those systems, the rule should be extraordinarily quick. In different words, it mustn't increase the process burden of the intermediator by any vital quantity. State –of-the-

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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 6, June 2016

art automatic Fingerprint Identification systems (AFIS) will method fingerprints at the speed of concerning one million matches per second. This suggests that the feature extraction and call rule accustomed mechanically sight altered fingerprints should be straightforward. 2. In the operational situations, the amount of people with altered fingerprints that may be terribly little. Keeping this in mind, the altered fingerprint detection rule ought to operate at a really little false positive rate, say 1% or lower, subjects that area unit suspected to own altered fingerprints can undergo a secondary scrutiny stage. 3. The altered fingerprint detector should be easily incorporated into AFIS. 1.2 Analysis of Altered Fingerprint Altered fingerprints made available to us by law enforcement agencies. It first 1) Determine the impact of fingerprint alteration on the matching performance, 2) Categorize altered fingerprints in to three types: obliteration, distortion, and imitation assess the utility of an existing fingerprint quality measure in terms of altered fingerprint detection. The objective of this paper is to spotlight the significance of the problem, analyze altered fingerprints, and endorse an automatic detection algorithm for them. The above stated hassle involving altered fingerprints falls beneath a broader class of assaults called biometric darken photo. Darken photograph can be described as a deliberate try by an individual to mask his identity from a biometric machine by changing the biometric trait prior to its acquisition with the aid of the system. Examples include mutilating the ridges of one’s fingerprint through the usage of abrasive cloth, perturbing the texture of the iris by carrying theatrical lenses, or changing facial attributes consisting of nostril and lips through surgical procedures. In this observe, we will subject ourself with the problem of fingerprint darken picture for the subsequent motives: 1) Fingerprint-based biometric systems are much more widespread for large-scale identification than any other biometric modality; 2) It is relatively easy to alter one’s fingerprints using chemicals and abrasives compared to, say, one’s iris or face, where a more elaborate surgical procedure may be necessary; and 3) Mutilated fingerprints are being routinely encountered by law enforcement and immigration officials

in several countries, thereby underscoring the urgency of finding a solution to this problem. Session 1 gives the overview of Fingerprint and its related identification marks. Session 2 describes the analysis and survey. In session 3 is the implementation work the proposed algorithm and Image are implemented in Mat lab based platform for Finger print image analysis and detection. In session 4 is compared with their proposed algorithm and the result of this dissertation is presented and the discussions are made, we have stated about the result and discussion of our work. Finally in session 5 we narrated about the conclusion and future enhancement followed by references gives the summary and conclusion. II. RELATED WORK Fingerprint recognition represents the oldest technique of identity verification. To figure properly the top quality fingerprint image is amazingly very important for fingerprint verification. It is actually the real of the standard fingerprint image is full of noise. This noise should be eliminated by totally different technique within the existing system during this one kind as extraction technique [1]. It assures that solely helpful info is processed at the time of minutiaes extraction. Within the existing paper fingerprint orientation field dependableness ways overcomes issues of the earlier ways of missing the deduction of spurious singular points. The fingerprint could be a duplicate of a tip cuticle once an individual touches a swish surface, the tip of the finger characteristic transferred to the bottom surface. The Finger print picture is formed by the pattern of the ridges and valleys on the human fingertips. In existing methods all the matching approaches of the fingerprint is compared by accumulating proof from matching the individual person and native neighborhood structures. The worldwide structural relationships creating false positives quite common for this native neighborhood during this sufficiently capture. Every native neighborhood is related to structural properties that area unit invariant underneath translation and rotation. In real world, the usual of the fingerprint snapshot is afflicted via noise like smirched area created by over-inked area, breaks in ridges created by underneath-inked house, dynamical the point traits of fingerprint options as a result of skin resilient in nature, dry epidermis ends up in fragmented and low distinction ridges, wounds would rationale ridge discontinuities and sweat on fingerprints conjointly ends up in smudge marks and connects parallel ridges. The fingerprint might be a duplicate of a tip cuticle as soon as a person touches a graceful

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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 6, June 2016

surface, the tip cuticle characteristic transferred to the skin. The pattern of the ridges and valleys on the human fingertips forms the fingerprint pix. Singular motive detection is that the most problematic mission; it can be a necessary system for fingerprint snapshot alignment, fingerprint classification and fingerprint matching. Throughout this endorse a orientation dependability and singular reason function procedures. The fingerprint image is formed from sample of ridges and valleys they're the replica of the human fingertips. The fingerprint snapshot represents a approach of directed texture and has terribly made structural data at intervals the snapshot. This flow-like pattern varieties partner in nursing orientation field extracted from the design of valleys and ridges. Inside the big a part of fingerprint topologies the orientation subject is variety of swish whereas in some areas the orientation seems in a very discontinuous method. A totally particular method to systematically and precisely find the singular facets (core and delta) in fingerprint graphics. The method applied relies on the elevated fingerprint picture orientation dependability. Fingerprint realization represents the oldest process of identification [2]. Most fingerprint photos displays noise within the zones nearly the ends of the photograph, this noise is prompted through quite a lot of factors just like the movement of the finger on the instantaneous of the capture or the restrained strain within the lateral areas from scanner. This noise will have to be eliminated to assure that solely worthy data are going to be processed at the time of minutiaes extraction. Simply in case that weren't eliminated these noises, the algorithmic software could sight false trivialities because of the noise. So the image was cut in the course of a one hundred percentage in all of its facets taking under consideration that didn't do away with possess data of the fingerprint. The purpose of companion in nursing sweetening algorithmic application is to boost the clarity of the ridge structures inside the redeemable regions and mark the misplaced areas as too blatant for extra process. The bulk of the existing methods are supported the utilization of discourse filters whose parameters depend on the native ridge frequency and orientation. The removal of interior snapshot elements skills is viable. Once the removing of some limit image aspect is not feasible however exist an indoor pixel. A large variety of recognition algorithms are planned [3]. It embrace a survey of some trivia based mostly matching algorithms since these square measure out and away, the foremost wide used approach for fingerprint recognition. In international Matching approach, the matching method tries to at the same time align all points

quickly. In alternative words, the transformation perform is assumed to be international and identical in the slightest degree positions on the print. These any 2 instances of the fingerprint happiness to a similar individual square measure typically connected by a non-linear transformation during this assumption is found to be affordable during a sizable amount of things. The matching algorithmic rule tries to match points occurring inside a little native neighborhood. The general alignment for all the points is obtained by combining these native matches in some consistent manner. Native matching algorithms square measure a lot of study to non-linear distortion and partial overlaps when put next to international approaches. In native matching approaches, the fingerprint is matched by accumulating proof from matching individual native neighborhood structures. Every native neighborhood is related to structural properties that square measure invariant underneath translation and rotation. Native neighborhood doesn't sufficiently capture the worldwide structural relationships creating false positives quite common. Two steps as native structure matching and consolidation. In native structure matching step native structures square measure compared to derive candidate matches for every structure within the reference print. In Consolidation step the candidate matches square measure valid supported however it agrees to the worldwide match and a score is generated by consolidating all the valid matches. The planned algorithmic rule is powerful to nonlinearities since solely native neighborhoods square measure matched at every stage. Ambiguities in trivia pairings square measure solved by using associate degree optimisation approach. Native matches square measure created employing a dynamic programming based mostly algorithmic rule. The coupled BFS algorithmic rule provides a awfully generic means of consolidating the native matches. Fingerprint is that the pattern of ridges and valleys on the surface of a tip [5]. The ridges square measure black and therefore the valleys square measure white. Its orientation field is outlined because the native orientation the ridge-valley structures. The trivialities square measure outlined as ridge endings and bifurcations. The singular points may be viewed as points wherever the orientation field is discontinuous, which may be classified into two sorts as core and delta. There square measure basically two ways that to cypher the orientation field of fingerprint as filterbank primarily based approaches and gradient-based approaches. The filter-bank primarily based approaches square measure additional proof against noise than the latter however it's separate valued depending on the amount of filters and computationally high-priced. The orientation

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model is employed to compress, restore or synthesize the fingerprint pictures. Then it's attainable to develop new technique for fingerprint identification supported the ridge orientation model during which the coefficients of orientation model may be used for fingerprint matching. Since existing fingerprint quality assessment algorithms are designed to examine if an image contains sufficient information for matching, they have limited capability in determining if an image is a natural fingerprint or an altered fingerprint. III. PROPOSED METHOD In the non identified signal the STFT is a popular methods in signal processing to research. Right here lengthen its application to 2nd fingerprint portraits. High pleasant fingerprint photo is totally fundamental for fingerprint verification to figure competently. In real life, the satisfactory of the fingerprint photograph is affected by noise like smudgy area created by over-inked area, breaks in ridges created by using beneath-inked field, altering the point traits of fingerprint choices due to skin resilient in nature, dry dermis results in fragmented and low distinction ridges, wounds may just motive ridge discontinuities and sweat on fingerprints conjointly results in smudge marks and connects parallel ridges. The fingerprint is a reproduction of a fingertip stratum as soon as any person touches a graceful floor, the fingertip stratum attribute transferred to the skin. The pattern of the ridges and valleys on the human fingertips types the fingerprint images. Orientation Field Estimation

Orientation Field Discontinui ty Feature Level Fusion

Fingerpri nts

Minutiae Extraction

Minutiae Density Map Fig 3.1 Architectural Design

ST FT

Matching Result

The above fig 3.1 shows the design of the projected method is to examine the fingerprint. This algorithm has follows two steps. Step 1: Improve the quality of image (fingerprint) from the database. Step 2: Convert them into high quality fingerprint. Then the process is evaluating the algorithm. This result shows the detecting altered fingerprint. In law enforcement and boundary manage

applications Automated Fingerprint Identification Systems (AFIS) has heightened the need for making certain that these systems don't seem to be nonetheless compromised. While many problems linked to fingerprint procedure protection are investigated, together with the utilization of faux fingerprints for pretense identification, the crisis of fingerprint alteration or darken image has got little attention. Fingerprint darken snapshot to the deliberate alteration of the fingerprint pattern through an person for the goal of covering his identification. Several cases of fingerprint obfuscation were rumored within the press. Also the algorithm used to evaluate the large database of altered fingerprints. Further the proposed algorithm is to examine the fingerprint. Fingerprint recognition refers to automated method for verifying and to identify the person by using their Fingerprints. This system is to handle the problems such as false non-matching and distorted Fingerprints. Distorted Fingerprints are Detected and Rectified by using Novel algorithm and it will also used to detect the malicious users. Malicious users will provide the duplicate and distort finger prints to escape from the identification process. 3.1. Feature Extraction Feature extraction is a new special type of reduction in the form of dimensions. While transforming the input knowledge into the set of options is termed feature extraction. If the options extracted square measure rigorously chosen it is expected that the features set can extract the relevant data from the input file so as to perform the required task mistreatment this reduced illustration rather than the total size input. Feature extraction involves simplifying the amount of resources needed to explain an outsized set of knowledge accurately. 3.2 Segmentation Segmentation sub divides an input image into its constituent elements or objects. The level to which the subdivision is carried depends on the matter being resolved. Autonomous segmentation is one of the major difficult tasks in image process. Monochrome images usually or based mostly on the segmentation algorithms for one amongst to the basic properties of grey level values like separation and similarity. 3.3 Minutiae and Ridge Extraction The minutia extraction is difficult in low image quality image, therefore, other information such as ridge shape and texture could be used to match the fingerprints, although they are not too distinctive as the minutiae. There has been proposed method that explores the ridge feature. In this subsection is presented the extraction of finger code.

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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 6, June 2016

The finger code is a vector of fingerprint feature obtained by applying a convolution to the original image with a Gabor filter. In this vector are stored the value of the standard deviation of each region. 3.4 Image Generation From the corresponding real environment the fingerprint images are generated by the data collection method and here include the parallel individuality of those collected finger print images. 3.5 Fingerprint Recognition Fingerprint recognition has been used for security problems, in order to recognize suspects and sufferers for more than a few decades. Recognition is a development that assigns a make to a fingerprint based on the information provided by its descriptors. Fingerprint recognition refers to automated methodology for substantiative and to determine the person by mistreatment their Fingerprints. This system is to handle the issues like false non-matching and distorted Fingerprints. The efficiency of associate in nursing automatic sweetening algorithmic program depends on the extent to that they utilize discourse data. The filters themselves may be outlined in spatial or within the Fourier domain. Malicious users will give the duplicate and distort finger prints to flee from the identification method. 3.6 STFT Processing The Proposed matching formula to discover and rectify skin alteration based mostly on one fingerprint image to unravel the regression drawback. STFT is a well-known technique in signal processing to research non-stationary signals to increase its application to second fingerprint pictures. The performance of a fingerprint feature extraction and matching algorithm depends critically upon the quality of the input fingerprint image. Distortion detection handles the classification problem happens in registered ridge orientation map and amount map. Detection techniques square measure used to discover the malicious users and Rectification method used for rectifying the duplicate distorted fingerprints. Regression problem and distortion issues can be solved. Time duration is reduced in projected system compared with existing system. High quality fingerprints images square measure made and Security level high in negative fingerprint regression. Fingerprint recognition represents the oldest method of biometric identification. Most fingerprint images displays noise in the zones close to the ends of the image, this noise can be caused by various factors like the movement of the finger at the instant of the capture or the tiny pressure within the lateral areas from scanner. This

noise must be eliminated to assure that solely helpful info can be processed at the time of extraction. In case that weren't eliminated these noises, the algorithm may discover false trivia due to the noise. Therefore the image was cut in a 100% in every one of its sides taking under consideration that failed to eliminate own info of the fingerprint. Fingerprint recognition refers to automated method for verifying and to identify the person by using their Fingerprints. This system is to handle the problems such as false non-matching and distorted Fingerprints. Distorted Fingerprints are Detected and Rectified by using Novel algorithm and it will also used to detect the malicious users. Malicious users will provide the duplicate and distort finger prints to escape from the identification process. In existing system the degradation of fingerprint quality can be photometric (or) geometrical. The fingerprint recognition contains some problems like false non-matching and distorted Fingerprints. This problem occurs in negative recognition application such as watch list and duplication. False non-matches due to limited capability of existing fingerprint matchers. Detection method also used to detect the malicious users and Rectification method used for rectifying the duplicate distorted fingerprints. Time duration is reduced in proposed system compared with existing system. Proposed algorithm to detect and rectify skin distortion based on a single fingerprint image to solve the regression problem. Distortion detection handles the classification problem occurs in registered ridge orientation map and period map. Advantages of this method is High quality fingerprints images are produced, Security level high in negative fingerprint regression, Regression problem and distortion problems will be solved. IV. EXPERIMENTAL RESULTS The proposed rule was evaluated at two phases: finger level (one finger) and discipline level (all ten fingers). On the finger level, It review the performance of deciding upon between traditional and changed fingerprints. At the subject stage, It overview the efficiency of making a choice on between subjects with average fingerprints and people with altered fingerprints. Because most AFIS utilized in enforcement, national id, and border control applications process all ten fingerprints of a person, the area degree efficiency utilizes this know-how of the making use of domain.

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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 6, June 2016

False positive rate (%) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 2.0 3.0

Fusi on

Of Discontinuity

Minutiae Distribution

30 38 41 45 48 50 53 55 57 59 70 75

25 33 38 40 43 47 49 50 52 54 65 70

17 21 24 27 30 33 35 37 38 39 50 57

Table 4.1: Fusion, Of Discontinuity, Minutia Distribution

circumstances. However, imitated fingerprints problematic for each and every algorithms.

are

Fig 4. ROC curves of the proposed algorithm and the NFIQ2.0 criterion in detecting altered Fingerprints

V. SUMMARY AND CONCLUSION The success of automated fingerprint identification systems has caused some criminals to take extreme measures to ward off identification through sterilization their fingerprints. It is imperative to boost a procedure that may routinely to find altered fingerprints. To be had fingerprint great management computer code modules weren't designed to inform apart altered from average fingerprints. It has developed an algorithmic rule to robotically to find altered fingerprints. The underlying notion is that altered fingerprints regularly show uncommon ridge patterns. A collection of elements is extracted from the ridge orientation subject and so a support vector classifier is employed to classify the fingerprint as typical or altered. The proposed algorithmic rule used to be established victimization altered fingerprints synthesized in methods that by and large found out in operational circumstances and a bit variety of obtainable real altered fingerprints. It have not yet concept-a few imperative clue for detection altered fingerprints, namely scars, which ordinarily appear on the cuts on finger dermis. It's currently functioning on combining orientation area and scar information to extra beef up the detection cost of altered fingerprints.

LIBSVM with radial groundwork kernel operate is used for classification with 10-fold go-validation. The ratings output with the aid of LIBSVM are linearly scaled to the fluctuate 0 to 10. The normalized ranking is termed a measure of the fingerprintness of the enter fingerprint. When the fingerprintness of accomplice degree input picture is smaller than a preset threshold fee, the process raises an alarm to denote that the photo would be a manageable altered fingerprint. If this picture is certainly partner degree altered fingerprint, it is deemed to be a real confident; in any other case, it's deemed to be a false confident. Similarly, real negative indicates that a normal fingerprint is competently classified as common associate degreed false terrible shows that an altered fingerprint is just not detected as altered. If the altered subject is just too small, the evidence of alteration is tricky to become aware of. The ROC curves of three procedures for detecting altered fingerprints (orientation field discontinuity, minutiae distribution, and their feature stage fusion) and the NFIQ algorithm. Within the imitation case, the ridge structure may be very average even at the boundary of altered vicinity; the orientation subject is steady and there's insignificant REFERENCE abnormality in trivialities density on scars. ROC curves of Yoon, Student Member, IEEE, Jianjiang Feng, Member, the deliberate algorithmic software and the NFIQ [1]. Soweon IEEE, and Anil K. Jain, Fellow, IEEE, “Altered Fingerprints: Analysis algorithmic application in step with alteration style. Both and Detection” IEEE Transactions on Pattern Analysis And Machine blotted out and distorted fingerprints can be detected through Intelligence, Vol. 34, No. 2, March 2012 the planned algorithmic program at an identical accuracy, at [2]. Harold Cummins, “Attempts To Alter And Obliterate Finger-Prints” the same time NFIQ will solely examine blotted out Volume 25,Issue 6 March-April , Article 12,”Journal of Criminal Law and Criminology”

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International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 3, Issue 6, June 2016 [3]. Jianjiang Feng, Anil.K. Jain, and A. Ross, “Detecting Altered Fingerprints”, Proc. 20th International Conference. Pattern Recognition, pp. 1622-1625, Aug. 2010. [4]. Soweon Yoon, Eryun Liu, and Anil K. Jain, “On Latent Fingerprint Image Quality”, 5th International workshop on computational Forensics, Tsukuba, Japan, Nov11, 2012. [5]. C.I.Watson and C.L. Wilson “NIST Special Database 4, “NIST 8-Bit Gray Scale Images of Fingerprint Image Groups (FIGS)”, http:// www. nist. Gov / srd/nistsd4. htm, 2011. [6]. J. Zhou and Jinwei. Gu, “A Model-Based Method for the Computation of Fingerprints’ Orientation Field,” IEEE Trans. Image Processing, vol. 13, no. 6, pp. 821-835, 2004. [7]. A. Antonelli, R. Cappelli, D. Maio, and D. Maltoni, “Fake Finger Detection by Skin Distortion Analysis, “IEEE Trans. Information Forensics and Security”, vol. 1, no. 3, pp. 360-373, Sept. 2006. [8]. J. Feng, A.K. Jain, and A. Ross, “Detecting Altered Fingerprints,” Proc. 20th Int’l Conf. Pattern Recognition, pp. 1622-1625, Aug. 2010 [9]. M. Wong, S.-P. Choo, and E.-H. Tan, “Travel Warning with Capecitabine,” Annals of Oncology, vol. 20, p. 1281, 2009 [10]. K. Wertheim, “An Extreme Case of Fingerprint Mutilation,” J. Forensic Identification, vol. 48, no. 4, pp. 466-477, 1998 [11]. History of Fingerprint http://jimfisher.edinboro.edu/forensics/fire /print.html

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[12]. J. Feng, A. K. Jain, A. Ross, "Fingerprint Alteration", MSU Technical Report, MSU-CSE-09-30, Dec. 2009 [13]. Xudong Jiang, Manhua Liu, Alex C. Kot “Fingerprint Retrieval for Identification”, IEEE Transactions on Information Forensics and Security, Vol.1, no.4, 2006. [14]. Jin Bo, Tang Hua Ping, Xu Ming Lan, “Fingerprint Singular Point Detection Algorithm by Poincare Index”, WSEAS Transactions in system, Issue 12, Vol. 7, 2008 [15]. F.A. Afsar, M. Arif and M. Hussain, “Fingerprint Identification and Verification System using Minutiae Matching”, National Conference on Emerging Technologies, 2004. [16]. Jie Zhou, Jinwel Gu, “Model-Based Method for the Computation of Fingerprints Orientation Field”, IEEE Transactions on Image Processing, Vol 13, No. 6, 2004. [17]. J.P.Jayan, Dr.Jeyakumar, V.G.Ranjith, “A Singular Points Detection Based fingerpoint Representation and Matching algorithm: Asurvey”, in IJARTET, vol 3, Issue 3, March 2016.