Detection and rectification of distorted fingerprints

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DETECTION AND RECTIFICATION OF DISTORTED FINGERPRINTS
ABSTRACT
Elastic distortion of fingerprints is one of the major causes for false non-match. While
this problem affects all fingerprint recognition applications, it is especially dangerous in negative
recognition applications, such as watch list and reduplications applications. In such applications,
malicious users may purposely distort their fingerprints to evade identification. In this paper, we
proposed novel algorithms to detect and rectify skin distortion based on a single fingerprint
image. Distortion detection is viewed as a two-class classification problem, for which the
registered ridge orientation map and period map of a fingerprint are used as the feature vector
and a SVM classifier is trained to perform the classification task. Distortion rectification (or
equivalently distortion field estimation) is viewed as a regression problem, where the input is a
distorted fingerprint and the output is the distortion field. To solve this problem, a database
(called reference database) of various distorted reference fingerprints and corresponding
distortion fields is built in the offline stage, and then in the online stage, the nearest neighbor of
the input fingerprint is found in the reference database and the corresponding distortion field is
used to transform the input fingerprint into a normal one. Promising results have been obtained
on three databases containing many distorted fingerprints, namely FVC2004 DB1, Tsinghua
Distorted Fingerprint database, and the NIST SD27 latent finer print database.

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Detection and rectification of distorted fingerprints

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DETECTION AND RECTIFICATION OF DISTORTED FINGERPRINTS

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bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry Nexgen Technology Address: Nexgen Technology No :66,4th cross,Venkata nagar, Near SBI ATM, Puducherry. Email Id: praveen@nexgenproject.com. www.nexgenproject.com Mobile: 9751442511,9791938249 Telephone: 0413-2211159. NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km

Detection and rectification of distorted fingerprint

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Detection and rectification

jpstudcorner

Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Nagar, Chennai-83. Landmark: Next to Kotak Mahendra Bank/Bharath Scans Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry - 9 Landmark: Opp. to Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690

IEEE PROJECTS 2015 1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider. It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training. Dot Net DOTNET Project Domain list 2015 1. IEEE based on datamining and knowledge engineering 2. IEEE based on mobile computing 3. IEEE based on networking 4. IEEE based on Image processing 5. IEEE based on Multimedia 6. IEEE based on Network security 7. IEEE based on parallel and distributed systems Java Project Domain list 2015 1. IEEE based on datamining and knowledge engineering 2. IEEE based on mobile computing 3. IEEE based on networking 4. IEEE based on Image processing 5. IEEE based on Multimedia 6. IEEE based on Network security 7. IEEE based on parallel and distributed systems ECE IEEE Projects 2015 1. Matlab project 2. Ns2 project 3. Embedded project 4. Robotics project Eligibility Final Year students of 1. BSc (C.S) 2. BCA/B.E(C.S) 3. B.Tech IT 4. BE (C.S) 5. MSc (C.S) 6. MSc (IT) 7. MCA 8. MS (IT) 9. ME(ALL) 10. BE(ECE)(EEE)(E&I) TECHNOLOGY USED AND FOR TRAINING IN 1. DOT NET 2. C sharp 3. ASP 4. VB 5. SQL SERVER 6. JAVA 7. J2EE 8. STRINGS 9. ORACLE 10. VB dotNET 11. EMBEDDED 12. MAT LAB 13. LAB VIEW 14. Multi Sim CONTACT US 1 CRORE PROJECTS Door No: 214/215,2nd Floor, No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai, Tamin Nadu, INDIA - 600 026 Email id: 1croreprojects@gmail.com website:1croreprojects.com Phone : +91 97518 00789 / +91 72999 51536

Detection and rectification of distorted fingerprints

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Abstract— In the field of biometric modality fingerprint is considered to be one of the most widely used method for individual identity. The fingerprint authentication is used in most application for security purpose. In the biometric systems, the input images are binarized and feature is extraction. The Minutiae matching in fingerprint identification is done by identifying the minutiae point of interest and their relationship. The validation testing in the proposed system using the method of K- fold cross validation by using two , a training set and test set of images to find the appropriate image that matches the input image ,increase the accuracy of recognition by reducing the false acceptance rate of the system.

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Face recognition is A technique that has been widely used in various important field, this process helps in the identification of an individual by a machine for the purpose of security and ease of work. The n ormal technique of face recognition usually works bet ter when there are multiple samples for a single person (MSSP) is available. In present applications where this technique is to be used such as in social ne tworks, security systems, identification cards there is only a single sample per person (SSPP) that is readily available. This less availability of the samples causes failure in the working of conventional face recognition techniques which require multiple samples for a particular individual. To overcome this drawback which sets back the system from the accurate functioning of face recognition this paper puts forward a novel technique which makes use of discriminative multi - manifold analysis (DMMA) that extracts distinctive features using image patches. Recognition is done by the process of manifold to ma nifold matching. Hence there is an increment in the accuracy rate of face recognition.

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This paper presents a novel method to recognize stem - calyx of an apple using shape descriptors. The main drawback of existing apple grading techniques is that stem - calyx part of an apple is treated as defects, this leads to poor grading of apples. In order to overcome this drawback, we proposed an approach to recognize stem-calyx and differentiated from true defects based on shape features. Our method comprises of steps such as segmentation of apple using grow-cut method, candidate objects such as stem-calyx and small defects are detected using multi-threshold segmentation. The shape features are extracted from detected objects using Multifractal, Fourier and Radon descriptor and finally stem-calyx regions are recognized and differentiated from true defects using SVM classifier. The proposed algorithm is evaluated using experiments conducted on apple image dataset and results exhibit considerable improvement in recognition of stem-calyx region compared to other techniques.

Multiple features based fingerprint identification system

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Abstract Security has become major issue now a day. In order to prevent unauthorized access of confidential data there is a need for accurate and reliable personal identification system. So, biometric based identification system is one of the best solutions. Fingerprint based system is one of oldest biometric identification systems. It is used in many commercial and security applications. Even with advent of technology in fingerprint identification system, the accurate extraction and matching of features from a fingerprint image is a challenging task. The task is much more challenging when fingerprint is affected by non-linear deformations such as rotation and translation. In this paper, fingerprint identification system using improved feature vector based algorithm is presented. In the algorithm Gabor filter is implemented to enhance the fingerprint image. The salient features minutiae (ridge endings) and reference point are extracted from the image. The Euclidian distances between reference point and each minutiae point are calculated and are arranged in ascending order. These are stored in database as feature vectors. The fingerprint matching is done based on the similarity rate between the feature vector of input fingerprint and the feature vectors stored in the database. Algorithms are implemented using Visual Studio 2010 in C++ language using Open CV libraries and tested on the fingerprint database created in the laboratory. Key Words: Fingerprint, Minutiae, Reference point, Euclidian distance, Similarity rate, Identification

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In this paper, a minutiae-based algorithm for fingerprint pattern recognition and matching is proposed. The algorithm uses the distance between the minutiae and core points to determine the pattern matching scores for fingerprint images. Experiments were conducted using FVC2002 fingerprint database comprising four datasets of images of different sources and qualities. False Match Rate (FMR), False Non-Match Rate (FNMR) and the Average Matching Time (AMT) were the statistics generated for testing and measuring the performance of the proposed algorithm. The comparative analysis of the proposed algorithm and some existing minutiae based algorithms was carried out as well. The findings from the experimental study were presented, interpreted and some conclusions were drawn.

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Detection and Rectification of Distorted Fingerprints

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Nexgen Technology Address: Nexgen Technology No :66,4th cross,Venkata nagar, Near SBI ATM, Puducherry. Email Id: praveen@nexgenproject.com. www.nexgenproject.com Mobile: 9751442511,9791938249 Telephone: 0413-2211159. NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km

Detection and Rectification of Distorted Fingerprints

1crore projects

Separation of overlapping latent fingerprints

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Separation of overlapping latent fingerprints

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Latent fingerprints lifted from crime scenes often contain overlapping prints, which are difficult to separate and match by state-of-the-art fingerprint matchers. The methods that have been proposed to separate overlapping fingerprints and successful matching previously suffer from limited accuracy of the estimated orientation field. In this paper, the robustness of overlapping fingerprints separation is increased, particularly for low quality images. This algorithm reconstructs the orientation fields of component prints by modeling fingerprint orientation fields. To facilitate this, orientation cues of component fingerprints are utilized, which are manually marked by fingerprint examiners. The effectiveness of this model has been evaluated.

Enhanced Latent Fingerprint Segmentation through Dictionary Based Approach

Editor IJMTER

The accuracy of latent finger print matching compared to roll and plain finger print matching is significantly lower due to background noise, poor ridge quality and overlapping structured noise in latent images. In this paper the proposed algorithm is dictionary-based approach for automatic segmentation and enhancement towards the goal of achieving “lights out” latent identifications system. Total variation decomposition model with L1 fidelity regularization in latent finger print image remove background noise. A coarse to fine strategy is used to improve robustness and accuracy. It improves the computational efficiency of the algorithm.

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Fingerprint recognition is one of the methods used in biometric system. Most of the biometric systems which are used for human identification or person’s identification. In this paper we are discussing minutiae matching and discrete wavelet transform and comparison of these two in fingerprint recognition. In this paper, firstly it uses fingerprint identification and performance in terms of equal error rate and then by calculating using discrete wavelet transform. The main aim of this paper is to create performing and accurate program for fingerprint identification.

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Fingerprints are usually used in recognition of a person's identity because of its uniqueness, stability. Today also the matching of incomplete or partial fingerprints remains challenge. The current technology is somewhat mature for matching ten prints, but matching of partial fingerprints still needs a lot of improvement. Automatic fingerprint identification techniques have been successfully adapted to both civilian and forensic applications. But this Fingerprint identification system suffers from the problem of handling incomplete prints and discards any partial fingerprints obtained. Level 2 features are very efficient if the quality of achievement decreases the number of level 2 features will not be enough for establishing high accuracy in identification. In such cases pores (level 3 features) can be used for partial fingerprint matching with the help of suitable technique local binary pattern features. Local binary pattern feature is used to match the pore against with full fingerprints. The first step involves extracting the pores from the partial image. These pores act as anchor points and sub window (32*32) is formed surrounding the pores. Then rotation invariant LBP histograms are obtained from the surrounding window. Finally chi-square formula is used to calculate the minimum distance between two histograms to find best matching score

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Character recognition in scene images is one of the most fascinating and challenging areas of pattern recognition with various practical application potentials. It can contribute immensely to the advancement of an automation process and can improve the interface between man and machine in many applications. Some practical application potentials of character recognition system are: reading aid for the blind, traffic guidance systems, tour guide systems, location aware systems and many more. In this work, a novel method for recognizing basic Kannada characters in natural scene images is proposed. The proposed method uses zone wise horizontal and vertical profile based features of character images. The method works in two phases. During training, zone wise vertical and horizontal profile based features are extracted from training samples and neural network is trained. During testing, the test image is processed to obtain features and recognized using neural network classifier. The method has been evaluated on 490 Kannada character images captured from 2 Mega Pixels cameras on mobile phones at various sizes 240x320, 600x800 and 900x1200, which contains samples of different sizes, styles and with different degradations, and achieves an average recognition accuracy of 92%. The system is efficient and insensitive to the variations in size and font, noise, blur and other degradations.

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Fingerprint recognition is one of the oldest and most popular biometric technologies and it is used in criminal investigations, civilian, commercial applications, and so on. Fingerprint matching is the process used to determine whether the two sets of fingerprints details come from the same finger or not. This work focuses on feature extraction and minutiae matching stage. There are many matching techniques used for fingerprint recognition systems such as minutiae based matching, pattern based matching, Correlation based matching, and image based matching. A new method based upon Principal Component Analysis (PCA) for fingerprint enhancement is proposed in this paper. PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. In the proposed method image is first decomposed into directional images using decimation free Directional Filter bank DDFB. Then PCA is applied to these directional fingerprint images which gives the PCA filtered images. Which are basically directional images? Then these directional images are reconstructed into one image which is the enhanced one. Simulation results are included illustrating the capability of the proposed method.

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This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks (CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations. When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10 and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing accuracy improvements over previous techniques. The results indicate that the combination of the volumetric input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating adversary training.

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Detection and rectification of distorted fingerprints

1. DETECTION AND RECTIFICATION OF DISTORTED FINGERPRINTS ABSTRACT Elastic distortion of fingerprints is one of the major causes for false non-match. While this problem affects all fingerprint recognition applications, it is especially dangerous in negative recognition applications, such as watch list and reduplications applications. In such applications, malicious users may purposely distort their fingerprints to evade identification. In this paper, we proposed novel algorithms to detect and rectify skin distortion based on a single fingerprint image. Distortion detection is viewed as a two-class classification problem, for which the registered ridge orientation map and period map of a fingerprint are used as the feature vector and a SVM classifier is trained to perform the classification task. Distortion rectification (or equivalently distortion field estimation) is viewed as a regression problem, where the input is a distorted fingerprint and the output is the distortion field. To solve this problem, a database (called reference database) of various distorted reference fingerprints and corresponding distortion fields is built in the offline stage, and then in the online stage, the nearest neighbor of the input fingerprint is found in the reference database and the corresponding distortion field is used to transform the input fingerprint into a normal one. Promising results have been obtained on three databases containing many distorted fingerprints, namely FVC2004 DB1, Tsinghua Distorted Fingerprint database, and the NIST SD27 latent finer print database.

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