Detecting and Improving Distorted Fingerprints using rectification techniques.

In this detection and improving distorted fingerprint using rectification techniques like SVM, PCA classifier etc.
In this ppt a distorted fingerprint is taken and improve that distorted fingerprint into normal one.

1. DETECTION AND IMPROVING DISTORTED FINGERPRINTS USING RECTIFICATION TECHNIQUE T ea m M embe rs: SANDIPAN PAUL B120884316 VAIBHAV DALVI B120884231 ADITYA CHAVARE B120884225

2. CONTENT Introduction Existing System Limitations of existing system Motivation Problem Statement System Architecture Modules in the Project Mathematical Model UML System Requirement Advantage of Proposed System Limitation of Proposed System Conclusion and Future Scope References Literature Survey

3. INTRODUCTION Tra d it i o n a l , s e c u ri t y is by what we: – K n ow (PIN, P a ssword) – H a ve (Key, smart card) • Bo t h of these s e c u rity mea s u r es can be hacked stolen forgotten dupli c ated . Mo d e r n, s e c u ri t y is by we: – Are ( B o dy) • It is not ea s y to c r eate a c o py be c au s e of its unique and high a c c u r a c y .

4. Com p ari s on Betw e en Bio m etric B i o met ri c T ec hno l og y A cc u r acy Co st D e v i ce R e qu ir ed S o c i al A cc ep ta b ili ty DN A H igh H igh T e s t Equip m ent Low I ris re c ognit i on H igh H igh C a m era M edium -Low R e t ina sc an H igh H igh C a m era Low F acial recogni t ion M edi u m - M edi u m C a m era H igh Low Voi c e re c ogni t ion M edium M edium M i c rophon e , H igh t elephone H and geo m e t ry M edium - Low S c anner H igh Low F i ng er p ri n t H i g h M e d i u m Sca nn er M e d i u m Signature re c ognit i o n Low M edium Op t ic pen, H igh t ouch pa n el

5. Existing System If the fingerprint is low quality or distorted due to some reason , then the authorized person don’t get authentication . Sometime malicious users purposely try to reduce fingerprint quality to prevent fingerprint system from finding their true identity .

6. Limitation of Existing System Problem in recognizing low quality fingerprints and rectify it. MOTIVATION Geometrical degradation due to skin distortion has not yet received sufficient attention, despite of the importance of this problem .This is the problem we attempts to address.

7. PROBLEM STATEMENT Low quality fingerprint due to skin distortion has not yet received sufficient attention. So our proposed system will try to overcome geometrical degradation.

8. PROPOSED SYSTEM To design a system that will recognize and rectify the distortion of fingerprint. After rectification, fingerprint is compared to the database.

9. SYSTEM ARCHITECTURE

10. MODULES Fingerprint Registration Level 1 features ( grey scale , orientation map, period map ) are extracted. Distorted fingerprint rectification If we can estimate the distortion field d from the given distorted fingerprint, we can easily rectify it into the normal fingerprint by applying the inverse of D. Distortion Field Estimation by Nearest Neighbor Search In this we retrieve its nearest neighbor in the distorted reference fingerprint database and then use the inverse of the corresponding distortion field to rectify the distorted input fingerprint.

11. MATHEMATICAL MODEL Set Theory: S={s, e, X, Y, } Where, s = Start of the program. 1)Log in user. 2)Select the image. e = End of the program. 1)The image is distorted or normal. 2)If distorted, rectify to its normal form 3)Verify the user. X = Input of the program. Input should be fingerprint image. Y = Output of the program. Finally we get the image is distorted or normal and will be rectified. X, Y U Let U be the Set of System. U= {Client, I, F, O, P, R, G, R }  

12. Where Client, I, F, O, P, R, G, R are the elements of the set. Client=User I=Input image (distorted fingerprint). F=Feature extraction. O=Orientation map. P=Period map. R=Reference database (distortion field estimation by nearest neighbor) G=Geometric transformation. R=Result or output (Rectified fingerprint). SPACE COMPLEXITY : The space complexity depends on Presentation and visualization of discovered patterns . More the storage of data more is the space complexity. TIME COMPLEXITY: Check patterns available in the database. If (n>1) then retrieving of information can be time consuming. So time complexity of algorithm is .  

13. Success Condition : P erform distortion rectification. Identify or recognize the fingerprint . Search the required information from available in Datasets. User gets result very fast according to their needs. Failure Condition : Sometime fingerprint quality is very low. Huge database can lead to more time consumption to get the information. Hardware failure. Software failure.

14. UsE CASE DIAGRAM

15. ACTIVITY DIAGRAM

16. DEPLOYMENT DIAGRAM

17. SYSTEM REQUIREMENT Processor - Pentium –III Speed - 1.1 GHz RAM - 256 MB Hard Disk - 20 GB Floppy Drive - 1.44 MB Key Board - Standard Windows Mouse -Two or Three Button Monitor - SVGA/LEC/LED Operating system : Windows 7,8,10 Coding Language : JDK 1.7 Database : MySQL 5 IDE : Eclipse Luna. Web Server: Apache Tomcat. SOFTWARE REQUIREMENT HARDWARE REQUIREMENT

18. ADVANTAGE OF PROPOSED SYSTEM LIMITATIONS PROPOSED SYSTEM System that will recognize and rectify the distortion for identification of error. S ystem will rectify the distortion completely System provide authentication to authorized user. In this system the authentication process is not 100% accurate.

19. CONCLUSION Distortion detection is done and in distortion rectification a nearest neighbor regression method is employed and fingerprint is compared to the database.

20. FUTURE WORK F uture work includes camera/projector lens distortion correction, to obtain more higher ridge depth precision of 3-D fingerprints .

21. Re f e r e n c es [1] Qinghai Gao , Xiaowen Zhang, “A Study of Distortion Effects on Fingerprint Matching ” Computer Science and Engineering 2012, 2(3): 37-42 .   [2] Yongchang Wang, Qi Hao , “Data Acquisition and Quality Analysis of 3-Dimensional Fingerprints”, Member, IEEE .   [3] P.Krishna Sai , A.Dheeraj , “Recognition besides Adjustment of Inaccurate Fingerprints Matching ”, International Journal of Research in Computer and Communication Technology, Vol 4, Issue 11, Nov- 2015.   [4] RAJESH PASHIKANTI, “A Novel Fake Fingerprint Minutia Matching Imaging Sensors Fabrication Materials”, ISSN 2319-8885 Vol.04,Issue.33, August-2015, Pages:6686-6691.   [5] Xuanbin Si, Jianjiang Feng, Jie Zhou, “Detecting Fingerprint Distortion from a Single Image ”, IEEE December 2012.  

22. LITERATURE SURVEY- PAPER REFERENCE 1. Distorted Fingerprint Matching Performance Improvement Existing fingerprint scanners are unable to scan fingerprints having mehandi drawn on finger. In this paper we focused on geometric distortions . 2. Experimental study of minutiae based algorithm for fingerprint matching The implementation of a proposed fingerprint pattern matching algorithm has been presented. The algorithm used the relative distances between the minutiae and the core points. 3. Detecting Fingerprint Distortion from a Single Image We refer a study on fingerprint distortion and develop an algorithm to detect fingerprint distortion from a single image which is captured using traditional fingerprint sensing techniques by analyzing ridge period and orientation information.

23. 3. Identify and Rectify the Distorted Fingerprints Wrong non-match rates of fingerprint matchers are very huge in the case of critically distorted fingerprints. This creates a security hole in automatic recognition of fingerprint systems which can be utilized by criminals and terrorists. 4. Survey on Detection and Rectification of Distorted Fingerprints Security hole in automatic fingerprint detection systems that could be used by criminals and terrorists. So, building up of fingerprint distortion scrutiny and reformation algorithms to fill the hole is a must. 5. Recognition besides Adjustment of Inaccurate Fingerprints Matching The implementation of a new fingerprint pattern matching algorithm has been presented. The algorithm used the relative distances between the minutiae and the core points..

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