FINGERPRINT RECOGNITION AND MATCHING TECHNOLOGY Introduction As the disruptive technology is changing the landscape of our societies or environments, Biometric authentication is finding more and more parts of the human body to prove we really are who we say we are. 'Biometrics' refers to the identification of human physical and behavioral characteristics, such as fingers, hands, ears, teeth, veins, voice and eyes. This concept of biometric authentication means biometrics are used to authenticate the body parts themselves as it is difficult to steal them, lose them, or duplicate them. Biometric authentication is a vital element in security due to unauthorised immigration, visa fraud, and border intrusion; it is increasingly being implemented at security checkout points at airports. More recently, increasing identity fraud has created a growing need for biometric technology for positive person identification in a number of non-forensic applications. Is this person authorized to enter this facility? Is this individual entitled to access the privileged information? Is the given service being administered exclusively to the enrolled users? Answers to questions such as these are valuable to business and government organizations. Since biometric identifiers cannot be easily misplaced, forged, or shared, they are considered more reliable for personal identification than traditional token or knowledge based methods. Human fingerprints are rich in a detail which is known as minutiae, which can be used as identification marks for fingerprint verification. The objective of this project is to explore the fingerprint recognition processes on minutia based matching which is quiet frequently used in various fingerprint algorithms and techniques .The approach of this project is find out how the minutia points are extracted from the fingerprint images and after that between two fingerprints we are performing the fingerprint matching. Image enhancement, image segmentation, minutia extraction and minutia matching these stages are the main themes of our project. This project is coded in MATLAB. Fingerprint Anatomy: A fingerprint is the composition of many ridges and furrows. Finger prints can‟t distinguished by their ridges and furrows. It can be distinguished by Minutia, which are some abnormal points on the ridges. Minutia is divided in to two parts such as: termination and bifurcation. Termination is also called ending and bifurcation is also called branch. Again minutia consists of ridges and furrows. Valley is also referred as furrow. In the course of this study we shall be looking at two fingerprint recognition problems which can be grouped into two sub-domains such as: i) fingerprint verification ii) fingerprint identification. Fingerprint verification is the method where we compare a claimant fingerprint with an enrolee fingerprint, where our aim is to match both the fingerprints. This method is mainly used to verify a person’s authenticity. For verification a person needs to his or her fingerprint in to the fingerprint verification system. Then it is representation is saved in some compress format with the person’s identity and his or her name. Then it is applied to the fingerprint verification system so that the person’s identity can be easily verified. Fingerprint verification is also called, one-toone matching. Fingerprint identification is mainly used to specify any person’s identity by his fingerprint. Identification has been used for criminal fingerprint matching. Here the system matches the fingerprint of unknown ownership against the other fingerprints present in the database to associate a crime with identity. This process is also called, one-to-many matching. Identification is traditionally used for solve crime and catch thieves. Additionally, one of the objectives of this project is to present a high level overview of fingerprint sensing and matching technology so as to know or gain insights into the strengths and limitations of the automation in matching fingerprints. In an attempt to analyzing fingerprint recognition system, we discovered system level design, which consists of a sensor, optical and semi-conductor, minutia extractor-made up of preprocessing, muntial extractor and post processing. Again pre processing stage is divided in to three sub stages such as:- i) image enhancement ii) image binarization iii) image segmentation. For image enhancement we shall use two methods such as:- histogram equalization and Fourier transform. After enhancing the image we shall binaries the image for that we used the locally adaptive threshold method. For image segmentation we preferred a three-step approach such as :i) block direction estimation ii) segmentation by direction intensity iii) Region of Interest (ROI) extraction by Morphological operations. Minutia extraction stage is divided in to two sub stages such as:- i) fingerprint ridge thinning and ii) minutia marking We used iterative parallel thinning algorithm for minutia extraction stage. Ridge thinning is used to eliminate the redundant pixels of the ridges 16 till the ridges are of one pixel wide. The minutia marking is quite simple task. Here crossing number (CN) concept is used. For the post processing stage, it has only one sub step that is:- removal of false minutia. Also a novel representation for bifurcations is proposed to unify terminations and bifurcation. After testing the set of minutia set of points of two finger print image we perform Minutiae Matching to check whether they belong to the same person or not. It includes two consecutive stages: i) alignment stage ii) match stage. In conclusion, experimental results will show the Performance evaluation index that will possibly display two types of performance evaluation indexes to determine the performance of a fingerprint recognition system such as:- False Rejection Rate (FRR): Sometimes the biometric security system may incorrectly reject an access attempt by an authorized user. To measure these types of incidents FAR is basically used. A system’s FRR basically states the ratio between the number of false rejections and the number of identification attempts. False Acceptance Rate (FAR): Sometimes the biometric security system may incorrectly accept an access attempt of an unauthorized user. To measure these types of incidents FAR is basically used. A system‟s FAR basically states the ratio between the number of false acceptances and the number of identification attempts. Above all, the effort of this project to understand how the Fingerprint Recognition is used in many applications like biometric measurements, solving crime investigation and also in security systems. From minutiae extraction to minutiae matching all stages are included in this implementation which generates a match score. Various standard techniques are used in the intermediate stages of processing. FINGERPRINT RECOGNITION AND MATCHING TECHNOLOGY Introduction As the disruptive technology is changing the landscape of our societies or environments, Biometric authentication is finding more and more parts of the human body to prove we really are who we say we are. 'Biometrics' refers to the identification of human physical and behavioral characteristics, such as fingers, hands, ears, teeth, veins, voice and eyes. This concept of biometric authentication means biometrics are used to authenticate the body parts themselves as it is difficult to steal them, lose them, or duplicate them. Biometric authentication is a vital element in security due to unauthorised immigration, visa fraud, and border intrusion; it is increasingly being implemented at security checkout points at airports. More recently, increasing identity fraud has created a growing need for biometric technology for positive person identification in a number of non-forensic applications. Is this person authorized to enter this facility? Is this individual entitled to access the privileged information? Is the given service being administered exclusively to the enrolled users? Answers to questions such as these are valuable to business and government organizations. Since biometric identifiers cannot be easily misplaced, forged, or shared, they are considered more reliable for personal identification than traditional token or knowledge based methods. Human fingerprints are rich in a detail which is known as minutiae, which can be used as identification marks for fingerprint verification. The objective of this project is to explore the fingerprint recognition processes on minutia based matching which is quiet frequently used in various fingerprint algorithms and techniques .The approach of this project is find out how the minutia points are extracted from the fingerprint images and after that between two fingerprints we are performing the fingerprint matching. Image enhancement, image segmentation, minutia extraction and minutia matching these stages are the main themes of our project. This project is coded in MATLAB. Fingerprint Anatomy: A fingerprint is the composition of many ridges and furrows. Finger prints can‟t distinguished by their ridges and furrows. It can be distinguished by Minutia, which are some abnormal points on the ridges. Minutia is divided in to two parts such as: termination and bifurcation. Termination is also called ending and bifurcation is also called branch. Again minutia consists of ridges and furrows. Valley is also referred as furrow. In the course of this study we shall be looking at two fingerprint recognition problems which can be grouped into two sub-domains such as: i) fingerprint verification ii) fingerprint identification. Fingerprint verification is the method where we compare a claimant fingerprint with an enrolee fingerprint, where our aim is to match both the fingerprints. This method is mainly used to verify a person’s authenticity. For verification a person needs to his or her fingerprint in to the fingerprint verification system. Then it is representation is saved in some compress format with the person’s identity and his or her name. Then it is applied to the fingerprint verification system so that the person’s identity can be easily verified. Fingerprint verification is also called, one-toone matching. Fingerprint identification is mainly used to specify any person’s identity by his fingerprint. Identification has been used for criminal fingerprint matching. Here the system matches the fingerprint of unknown ownership against the other fingerprints present in the database to associate a crime with identity. This process is also called, one-to-many matching. Identification is traditionally used for solve crime and catch thieves. Additionally, one of the objectives of this project is to present a high level overview of fingerprint sensing and matching technology so as to know or gain insights into the strengths and limitations of the automation in matching fingerprints. In an attempt to analyzing fingerprint recognition system, we discovered system level design, which consists of a sensor, optical and semi-conductor, minutia extractor-made up of preprocessing, muntial extractor and post processing. Again pre processing stage is divided in to three sub stages such as:- i) image enhancement ii) image binarization iii) image segmentation. For image enhancement we shall use two methods such as:- histogram equalization and Fourier transform. After enhancing the image we shall binaries the image for that we used the locally adaptive threshold method. For image segmentation we preferred a three-step approach such as :i) block direction estimation ii) segmentation by direction intensity iii) Region of Interest (ROI) extraction by Morphological operations. Minutia extraction stage is divided in to two sub stages such as:- i) fingerprint ridge thinning and ii) minutia marking We used iterative parallel thinning algorithm for minutia extraction stage. Ridge thinning is used to eliminate the redundant pixels of the ridges 16 till the ridges are of one pixel wide. The minutia marking is quite simple task. Here crossing number (CN) concept is used. For the post processing stage, it has only one sub step that is:- removal of false minutia. Also a novel representation for bifurcations is proposed to unify terminations and bifurcation. After testing the set of minutia set of points of two finger print image we perform Minutiae Matching to check whether they belong to the same person or not. It includes two consecutive stages: i) alignment stage ii) match stage. In conclusion, experimental results will show the Performance evaluation index that will possibly display two types of performance evaluation indexes to determine the performance of a fingerprint recognition system such as:- False Rejection Rate (FRR): Sometimes the biometric security system may incorrectly reject an access attempt by an authorized user. To measure these types of incidents FAR is basically used. A system’s FRR basically states the ratio between the number of false rejections and the number of identification attempts. False Acceptance Rate (FAR): Sometimes the biometric security system may incorrectly accept an access attempt of an unauthorized user. To measure these types of incidents FAR is basically used. A system‟s FAR basically states the ratio between the number of false acceptances and the number of identification attempts. Above all, the effort of this project to understand how the Fingerprint Recognition is used in many applications like biometric measurements, solving crime investigation and also in security systems. From minutiae extraction to minutiae matching all stages are included in this implementation which generates a match score. Various standard techniques are used in the intermediate stages of processing. ...