Fingerprint detection


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This is a complete report on Bio-metrics, finger print detection. It include what finger print is, how to scan and refin finger print, how the mechanism of its detection work, applications, etc

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Fingerprint detection

  1. 1. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 1 ABSTRACT “FINGERPRINT RECOGNITION SYSTEM” is a security facility provided to the users which supports decision to make on the access rights to the authorized users by authentication.This project extrapolates the necessary fingerprint data verification and enrollment and requires the users to make decisions taking high quality image, more responsibility, and accountability and making comparisons on the ridge patterns of the fingerprints of the users. This project is based on the fact that each person has a unique pattern of the fingerprint that differentiates him from others. Fingerprint recognition is a biometric technique for personal identification. Biometrics based fingerprint recognition provides one of the promising solutions for the security of the software and the domain of applying this techniques for security is increasing day by day. Biometric features also include speech, handwriting, face identification etc. Face identification is one of the popular techniques for personal identification, but may fail in certain situations where two people look very similar. Even the speech and handwriting recognition systems may fail in certain situations, Fingerprints’ being complex patterns has the advantage of being a passive, noninvasive system for personal identification and its success depends on solving the two problems:  Representation of the complex patterns of the fingerprints and  Matching these fingerprint patterns. This project uses both, algebraic and geometric features to representation fingerprint images. Here we divide both the existing finger print in the database and the scanned finger print into frames and compare the pixel values of the same and the user is authenticated based on the percentage of values being compared. The constraints of the percentage of fingerprint being matched can me modified as needed and hence the authentication can be made as strict as possible based on the criticality of its application.
  2. 2. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 2 TABLE OF CONTENTS Chapter no TITLE PAGE NO ABSTRACT 1. INTRODUCTION 5 1.1 BIOMETRIC SYSTEMS 5 1.1.1 What is biometric system? 5 1.1.2 Working of a Biometric System 6 1.1.3 Issues have to be address. 7 1.1.4 The most common biometrics. 8 1.1.5 What is Fingerprint? 12 2. FINGERPRINT DECTION 13 2.1 Finger Print Detection. 13 2.2 PRE-PROCSSING OF IMAGES 13 2.2.1 Binarization 14 2.2.2 THINNING: 14 Erosion: 15 Dilation: 15 2.2.3 Final Noise Removal 18 2.3 MINUTAE MATCHING 18 2.3.1 CHARACTERSTICS: 18 2.3.2 MINUTAE EXTRACTION 19 2.3.3 FINDING A RIDGE SUMMIT POINT: 19 TRACING A RIDGE: 20 2.4 PATTERN MATCHING 21 2.5 FINGERPRINT MATCHING & AUTHENTICATION 22 3 CONCLUSION 24 3.1 ADVANTAGES & DISADVANTAGES 24 3.1.1 DISADVANTAGES OF USING FINGERPRINT 24 3.1.2 ADVANTAGES OF USING FINGERPRINT 24 3.2 APPLICATIONS 25 4 BIBLIOGRAPHY 26
  3. 3. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 3 LIST OF FIGURES S.NO TITLE PAGE NO 1. Some of the biometrics 5 2. Biometric Market Report in the year 2002. 10 3. fingerprint 11 4. Effect of Binarization 13 5. Figure: Effect of Dilation 14 6. Figure: Effect of Block filter 15 7. Noise removal 15 8. Combined image of both the images 15 9. Crossing over 1 16 10. Crossing over 2 16 11. Crossing over 3 17 12. Figure: Effect of Spurs 17 13. Thinned image from block filtering 17 14. Impact of deleting short island segments 18 15. Figure: Ridge endings 18 16. Figure: Ridge bifurcation 18 17. Figure: Ridg ridges 19 18. Figure: Ridge enclosures 19 19. Figure: minutia attributes 19 20. Figure: RIDGE SUMMIT POINT: 20 21. Figure: Ridge tracing 21 22. Process of identification 23
  4. 4. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 4 Chapter1 INTRODUCTION- 1.1 BIOMETRIC SYSTEMS 1.1.1 What is biometric system? A biometric system is essentially a pattern recognition system that recognizes a person by determining the authenticity of a specific physiological and/or behavioral characteristic possessed by that person. An important issue in designing a practical biometric system is to determine how an individual is recognized. Depending on the application context, a biometric system may be called either a verification system or an identification system:· A verification system authenticates a person’s identity by comparing the captured biometric characteristic with her own biometric template(s) pre- stored in the system. It conducts one-to-one comparison to determine whether the identity claimed by the individual is true. A verification system either rejects or accepts the submitted claim of identity (Am I whom I claim I am?);· An identification system recognizes an individual by searching the entire template atabasefor a match. It conducts one-to-many comparisons to establish the identity of the individual. In an identification system, the system establishes a subject’s identity (or fails if the subject is not enrolled in thesystem database) without the subject having to claim an identity (Who am I?).
  5. 5. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 5 Some of the biometrics are: a)ear, b)face, c)facial thermo gram, d)hand thermo gram, e)hand vein, f)hand geometry, g)fingerprint, h)iris, i)retina, j)signature, k)voice. 1.1.2 Working of a Biometric System The term authentication is also frequently used in the biometric field, sometimes as a synonym for verification; actually, in the information technology language, authenticating a user means to let the system know the user identity regardless of the mode (verification or identification). The enrollment module is responsible for registering individuals in the biometric system database (system DB). During the enrollment phase, the biometric characteristic of an individual is first scanned by a biometric reader to produce a raw digital representation of the characteristic. A quality check is generally performed to ensure that the acquired sample can be reliably processed by successive stages. In order to facilitate matching, the raw digital representation is usually further processed by a feature extractor to generate a compact but expressive representation, called a template. Depending on the application, the template may be stored in the central database of the biometric system or be recorded on a magnetic card or smartcard issued to the individual. The verification task is responsible for verifying individuals at the point of access. During the operation phase, the user’s name or PIN (Personal
  6. 6. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 6 Identification Number) is entered through a keyboard (or a keypad); the biometric reader captures the characteristic of the individual to be recognized and converts it to a digital format, which is further processed by the feature extractor to produce a compact digital representation. The resulting representation is fed to the feature matcher, which compares it against the template of a single user (retrieved from the system DB based on the user’s PIN). In the identification task, no PIN is provided and the system compares the representation of the input biometric against the templates of all the users in the system database; the output is either the identity of an enrolled user or an alert message such as “user not identified.” Because identification in large databases is computationally expensive, classification and indexing techniques are often deployed to limit the number of templates that have to be matched against the input. 1.1.3 When choosing a biometric for an application the following issues have to be address.  Does the application need verification or identification? If an application requires an identification of a subject from a large database, it needs a scalable and relatively more distinctive biometric (e.g., fingerprint, iris, or DNA).  What are the operational modes of the application? For example, whether the application is attended (semi-automatic) or unattended (fully automatic), whether the users are habituated (or willing to be habituated) to the given biometrics, whether the application is covert or overt, whether subjects are cooperative or non-cooperative, and so on.  What is the storage requirement of the application? For example, an application that performs the recognition at a remote server may require a small template size.  How stringent are the performance requirements? For example, an application that demands very high accuracy needs a more distinctive biometric.
  7. 7. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 7  What types of biometrics are acceptable to the users? Different biometrics are acceptable in applications deployed in different demographics depending on the cultural, ethical, social, religious, and hygienic standards of that society. 1.1.4 The most common biometrics.  Ear: It is known that the shape of the ear and the structure of the cartilaginous tissue of the pinna are distinctive. The features of an ear are not expected to be unique to an individual. The ear recognition approaches are based on matching the distance of salient points on the pinna from a landmark location on the ear.  Face: The face is one of the most acceptable biometrics because it is one of the most common methods of recognition that humans use in their visual interactions. In addition, the method of acquiring face images is nonintrusive. Facial disguise is of concern in unattended recognition applications. It is very challenging to develop face recognition techniques that can tolerate the effects of aging, facial expressions, slight variations in the imaging environment, and variations in the pose of the face with respect to the camera.  Facial, hand, and hand vein infrared thermograms: The pattern of heat radiated by the human body is a characteristic of each individual body and can be captured by an infrared camera in an unobtrusive way much like a regular (visible spectrum) photograph. The technology could be used for covert recognition and could distinguish between identical twins. A thermogrambased system is non-contact and non-invasive but sensing challenges in uncontrolled environments, where heat-emanating surfaces in the vicinity of the body, such as, room heaters and vehicle exhaust pipes, may drastically affect the image acquisition phase. A related technology using near infrared imaging is used to scan the back of a clenched fist to determine hand vein structure. Infrared sensors are prohibitively expensive which a factor inhibiting widespread use of the thermograms.
  8. 8. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 8  Hand and finger geometry: Some features related to a human hand (e.g., length of fingers) are relatively invariant and peculiar (although not very distinctive) to an individual. The image acquisition system requires cooperation of the subject and captures frontal and side view images of the palm flatly placed on a panel with outstretched fingers. The representational requirements of the hand are very small (nine bytes in one of the commercially available products), which is an attractive feature for bandwidth- and memory-limited systems. Due to its limited distinctiveness, hand geometry-based systems are typically used for verification and do not scale well for identification applications. Finger geometry systems (which measure the geometry of only one or two fingers) may be preferred because of their compact size.  Iris: Visual texture of the human iris is determined by the chaotic morphogenetic processes during embryonic development and is posited to be distinctive for each person and each eye. An iris image is typically captured using a non-contact imaging process. Capturing an iris image involves cooperation from the user, both to register the image of iris in the central imaging area and to ensure that the iris is at a predetermined distance from the focal plane of the camera. The iris recognition technology is believed to be extremely accurate and fast.  Retinal scan: The retinal vasculature is rich in structure and is supposed to be a characteristic of each individual and each eye. It is claimed to be the most secure biometric since it is not easy to change or replicate the retinal vasculature. The image capture requires a person to peep into an eyepiece and focus on a specific spot in the visual field so that a predetermined part of the retinal vasculature may be imaged. The image acquisition involves cooperation of the subject, entails contact with the eyepiece, and requires a conscious effort on the part of the user. All these factors adversely affect public acceptability of retinal biometrics. Retinal vasculature can reveal some medical conditions (e.g., hypertension), which is another factor standing in the way of public acceptance of retinal scan-based biometrics.
  9. 9. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 9  Signature: The way a person signs his name is known to be a characteristic of that individual. Although signatures require contact and effort with the writing instrument, they seem to be acceptable in many government, legal, and commercial transactions as a method of verification. Signatures are a behavioral biometric that change over a period of time and are influenced by physical and emotional conditions of the signatories. Signatures of some people vary a lot: even successive impressions of their signature are significantly different. Furthermore, professional forgers can reproduce signatures to fool the unskilled eye.  Voice: Voice capture is unobtrusive and voice print is an acceptable biometric in almost all societies. Voice may be the only feasible biometric in applications requiring person recognition over a telephone. Voice is not expected to be sufficiently distinctive to permit identification of an individual from a large database of identities. Moreover, a voice signal available for recognition is typically degraded in quality by the microphone, communication channel, and digitizer characteristics. Voice is also affected by a person’s health (e.g., cold), stress, emotions, and so on. Besides, some people seem to be extraordinarily skilled in mimicking others.
  10. 10. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 10 Table comparing various biometric technologies High, Medium, and Low are denoted by H, M, and L, respectively. Biometric Market Report (International Biometric Group) estimated the revenue of various biometrics in the year 2002.
  11. 11. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 11 1.1.5 What is Fingerprint? A fingerprint is a textural image containing a large number of ridges that form groups of almost parallel curves. It has been established that fingerprint's ridges are individually unique and are unlikely to change during the whole life.
  12. 12. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 12 Chapter 2 FINGERPRINT DECTION 2.1 Finger Print Detection. The use of fingerprints as a biometric is both the oldest mode of computer- aided, personal identification and the most prevalent in use today. However, this widespread use of fingerprints has been and still is largely for law enforcement applications. There is expectation that a recent combination of factors will favor the use of fingerprints for the much larger market of personal authentication. These factors include: small and inexpensive fingerprint capture devices, fast computing hardware, recognition rate and speed to meet the needs of many applications, the explosive growth of network and Internet transactions, and the heightened awareness of the need for ease-of-use as an essential component of reliable security. This method has been widely used in criminal identification, access authority verification, financial transferring confirmation, and many other civilian applications. In the old days, fingerprint recognition was done manually by professional experts. But this task has become more difficult and time consuming. 2.2 PRE-PROCSSING OF IMAGES Following image capture to obtain the fingerprint image, image processing is performed. The ultimate objective of image processing is to achieve the best image by which to produce the correct match result. Steps Of Pre-Processing THINNINGBINARIZATION NOISE REMOVAL
  13. 13. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 13 2.2.1 Binarization  Image binarization is the process of turning a grayscale image to a black and white image.  In a gray-scale image, a pixel can take on 256 different intensity values while each pixel is assigned to be either black or white in a black and white image.  This conversion from gray-scale to black and white is performed by applying a threshold value to the image. A critical component in the binarization process is choosing a correct value for the threshold. The threshold values used in this study were selected empirically by trial and error. Figure: Effect of Binarization 2.2.2 THINNING: This thinning method to be done with Block Filtering method attempts to preserve the outermost pixels along each ridge This is done with the following steps: Step One: ridge width reduction This step involves applying a morphological process to the image to reduce the width of the ridges. Morphological is a means of changing a stem to adjust
  14. 14. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 14 its meaning to fit its syntactic and communicational context Two basic morphological processes are  Erosion  Dilation Erosion: Erosion thins objects in a binary image (ridge)In this project we are using the Dilation: A dilation process is used to thicken the area of the valleys in the fingerprint. Original Gray Image After Level Image Dilation Figure: Effect of Dilation Step Two: passage of block filter The next step involves performing a pixel-by pixel scan for black pixels across the entire image Note that in MATLAB, image rows are numbered in increasing order beginning with the very top of the image as row one. Similarly, columns are numbered in increasing order beginning with the leftmost side of the left to right scan continues until it covers the entire image. Next, a similar scan is performed across the image from right to left beginning at the pixel in row one and the last column.
  15. 15. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 15 Original image Image after block filter Figure: Effect of Block filter Step Three: removal of isolated noise Image with noise Image after noise removal Step Four: scan combination A value of two means that the pixel from each scan was white, while a value of zero indicates the pixel from each scan was black. Meanwhile, a value of one means that the pixel from one scan was black while the same pixel from the other scan was white. As a result, the new matrix needs to be adjusted to represent a valid binary image containing only zeros and ones. Specifically, all zeros and ones are assigned a value of zero (black pixel), Combined image of both the images
  16. 16. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 16 Step Five: elimination of one pixel from two-by-two squares of black Next, a new scan is conducted on the combined image to detect two-by-two blocks of black pixels which represent a location where a ridge has not been thinned to a one-pixel width. It is likely that some of these two-by two blocks were created by the combination of the previous scans. This problem can be compensated for by changing one pixel within the block from black to white, which reduces the width at that particular point from two pixels to one. At the same time, This operation can be performed by analyzing the pixels touching each individual black pixel. Note that each black pixel touches the three other black pixels within the two-by-two block. Therefore, there are only five other pixels that contain useful information. Step Six: removal of unwanted spurs Crossing over 1 Crossing over 2
  17. 17. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 17 Crossing over 3 With spurs after its removal Figure: Effect of Spurs Step Seven: removal of duplicate horizontal and duplicate vertical lines Thinned image from block filtering
  18. 18. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 18 2.2.3 Final Noise Removal Impact of deleting short island segments 2.3 MINUTAE MATCHING 2.3.1 CHARACTERSTICS: A fingerprint is a textural image containing a large number of ridges that form groups of almost parallel curves. It has been established that fingerprint's ridges are individually unique and are unlikely to change during the whole life. Although the structure of ridges in a fingerprint is fairly complex, it is well known that a fingerprint can be identified by its special features such as: Ridge endings: The ending of the ridges takes place at the middle as shown. Ridge bifurcation: The division of the ridges in the middle as shown
  19. 19. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 19 Short ridges: The small lines present in between two ridges as shown Ridge enclosures: These are the loops formed as shown in fig(c). 2.3.2 MINUTAE EXTRACTION For convenience, we represent a fingerprint image in reverse gray scale. That is, the dark pixels of the ridges are assigned high values where as the light pixels of the valleys are given low values. In a fingerprint, each minutia is represented by its location (x, y) and the local ridge direction Figure 4 shows the attributes of a fingerprint's minutia. The process of minutiae detection starts with finding a summit point on a ridge, and then continues by tracing the ridge until a minutia, which can be either a ridge ending or bifurcation, is encountered. 2.3.3 FINDING A RIDGE SUMMIT POINT: To find a summit point on a ridge, we start from a point x = (x1, x2) and compute the direction angle by φ using the gradient method. Then the vertical section orthogonal to the direction is constructed. The point in this
  20. 20. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 20 section with maximum gray level is a summit point on the nearest ridge. The direction angle φ at a point x mentioned above is computed as follows. A 9×9 neighborhood around x is used to determine the trend of gray level change. At each pixel u = (u1, u2) in this neighborhood, a gradient vector v(u) = (v1(u), v2(u)) is obtained by applying the operator h = (h1, h2) with to the gray levels in a neighborhood of u. That is, Where y runs over the eight neighboring pixels around u and g(y) is the gray level of pixel y in the image. The angle represents the direction of the unit vector t that is (almost) orthogonal to all gradient vectors v. That is, t is chosen so that is minimum. TRACING A RIDGE: The task of tracing a ridgeline to detect minutiae is described in the following algorithm. This algorithm also constructs a traced image of the fingerprint. Every time a new summit point of the ridge is found, its location in the traced image is assigned a high gray value and the surrounding pixels are given lower gray levels if they have not been marked.
  21. 21. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 21 Algorithm 1 (Ridge tracing): Start from a summit point x of a ridge. Repeat Compute the direction angle at x; Move ∝ pixels from x along the direction to another point y; Find the next summit point z on the ridge, which is the local maximum of the section orthogonal to direction at point y; Set x = z; Until point x is a termination point (i.e. a minutia or off valid area). Determine if the termination point x is a valid minutia, if so record it. End Algorithm 1 2.4 PATTERN MATCHING The more macroscopic approach to matching is called global pattern matching or simply pattern matching. In this approach, the flow of ridges is compared at all locations between a pair of fingerprint images. The ridge flow constitutes a global pattern of the fingerprint. Three fingerprint patterns are shown in Figure (Different classification schemes can use up to ten or so pattern classes, but these three are the basic patterns.) Two other features are sometimes used for matching: core and delta. (Figure) The core can be thought of as the center of the fingerprint pattern. The delta is a singular point from which three patterns deviate. The core and delta locations can be used as landmark locations by which to orient two fingerprints for subsequent matching – though these features are not present on all fingerprints. There may be other features of the fingerprint that are used in matching. For instance, pores can be resolved by some fingerprint sensors
  22. 22. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 22 and there is a body of work (mainly research at this time) to use the position of the pores for matching in the same manner that the minutiae are used. Size of the fingerprint, and average ridge and valley widths can be used for matching, however these are changeable over time. The positions of scars and creases can also be used, but are usually not used because they can be temporary or artificially introduced. 2.5 FINGERPRINT MATCHING AND AUTHENTICATION Reliably matching fingerprint images is an extremely difficult problem, mainly due to the large variability in different impressions of the same finger (i.e., large intra-class variations). The main factors responsible for the intra-class variations are: displacement, rotation, partial overlap, non-linear distortion, variable pressure, changing skin condition, noise, and feature extraction errors. Therefore, fingerprints from the same finger may sometimes look quite different whereas fingerprints from different fingers may appear quite similar (see Figure 1.14). Difficulty in fingerprint matching:  Fingerprint look different to an untrained eye but they are impressions of the same finger.  Fingerprint look similar to an untrained eye but they are from different fingers. Human fingerprint examiners, in order to claim that two fingerprints are from the same finger, evaluate several factors: i) global pattern configuration agreement, which means that two fingerprints must
  23. 23. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 23 be of the same type, ii) qualitative concordance, which requires that the corresponding minute details must be identical, iii) quantitative factor, which specifies that at least a certain number (a minimum of 12 according to the forensic guidelines in the United States) of corresponding minute details must be found, and iv) corresponding minute details, which must be identically inter-related. In practice, complex protocols have been defined for fingerprint matching and a detailed flowchart is available to guide fingerprint examiners in manually performing fingerprint matching. Given below is a figure showing the general method by which fingerprints are matched. Figure showing a general method as to how the finger print is matched and compared with an existing fingerprint from the database. Process of identification
  24. 24. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 24 CHAPTER 3 CONCLUSION 3.1 ADVANTAGES & DISADVANTAGES OF USING FINGERPRINT 3.1.1 DISADVANTAGES OF USING FINGERPRINT  There are some problems in collecting the second database:  The different age of the persons leads to a different size of the fingerprint  Some of the twins are children so there are scratches in the fingerprints  Some of them did not fully cooperate with the researchers, so most of the images of their fingerprints do not contain enough features to create an extraction. 3.1.2 ADVANTAGES OF USING FINGERPRINT  Prevents unauthorized use or access  Adds a higher level of security to an identification process  Eliminates the burden and bulk of carrying ID cards or remembering Pins  Heightens overall confidence of business processes dependent on personal identification.
  25. 25. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 25 3.2 APPLICATIONS  Criminal identification  Prison security  ATM  Aviation security  Border crossing controls  Database access  Door-Lock System  Safe Box  Simple Access Controller  Vehicle Control
  26. 26. BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION 26 BIBLIOGRAPHY i. ii. iii. iv. v. vi. vii. viii. ix. Baruch,O.Following", Pattern Recognition Letters, Vol. 8 No. 4, 1988, pp. 271-276. x. Nist image group’s fingerprint research. [Online; accessed 25-February-2010]. xi. Fvc2006 the fourth international fingerprint verification competition. res db2 a.asp. [Online; accessed 25-February-2010]. xii. Fvc2004 the third international fingerprint verification competition. [Online; accessed 25- February-2010]. xiii. S.A. Niyogi and E.H. Adelson. Analyzing and recognizing walking figures in xyt. CVPR, 94:469–474.