IEEE Projects 2015 | Detection and rectification of distorted fingerprints
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This document discusses a novel approach for detecting and rectifying distorted fingerprints, which is critical for fingerprint recognition systems, particularly in negative recognition applications. The proposed method employs machine learning algorithms to classify fingerprints as distorted or normal and utilizes a database of distorted reference fingerprints to correct the distortion. The system shows promising results in enhancing fingerprint recognition accuracy across multiple datasets.
IEEE Projects 2015 | Detection and rectification of distorted fingerprints
- 1. Detection and Rectification of Distorted Fingerprints
- 2. 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 watchlist and deduplication 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 fingerprint database. EXISTING SYSTEM:
- 3. Fingerprint matcher is very sensitive to image quality as observed where the matching accuracy of the same algorithm varies significantly among different datasets due to variation in image quality. A fingerprint recognition system can be classified as either a positive or negative system. In a positive recognition system, such as physical access control systems, the user is supposed to be cooperative and wishes to be identified. In a negative recognition system, such as identifying persons in watch lists and detecting multiple enrollment under different names, the user of interest (e.g., criminals) is supposed to be uncooperative and does not wish to be identified. In Existing System, since existing fingerprint quality assessment algorithms are designed to examine if an image contains sufficient information (say, minutiae) for matching, they have limited capability in determining if an image is a natural fingerprint or an altered fingerprint. Obliterated fingerprints can evade fingerprint quality control software, depending on the area of the damage. If the affected finger area is small, the existing fingerprint quality assessment software may fail to detect it as an altered fingerprint. DISADVANTAGES OF EXISTING SYSTEM: 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.
- 4. They require special force sensors or fingerprint sensors with video capturing capability They cannot detect distorted fingerprint images in existing fingerprint databases. They cannot detect fingerprints distorted before pressing on the sensor. However, allowing larger distortion in matching will inevitably result in higher false match rate. For example, if we increased the bounding zone around a minutia, many non-mated minutiae will have a chance to get paired. In addition, allowing larger distortion in matching will also slow down the matching speed. PROPOSED SYSTEM: In Proposed System was evaluated at two levels: finger level and subject level. At the finger level, we evaluate the performance of distinguishing between natural and altered fingerprints. At the subject level, we evaluate the performance of distinguishing between subjects with natural fingerprints and those with altered fingerprints This paper described a novel distorted fingerprint detection and rectification algorithm. For distortion detection, the registered ridge orientation map and period map of a fingerprint are used as the feature vector and a SVM classifier is trained to classify the input fingerprint as distorted or normal.
- 5. A nearest neighbor regression approach is used to predict the distortion field from the input distorted fingerprint and then the inverse of the distortion field is used to transform the distorted fingerprint into a normal one. ADVANTAGES OF PROPOSED SYSTEM: Fingerprint rectification algorithm consists of an offline stage and an online stage. In the offline stage, a database of distorted reference fingerprints is generated by transforming several normal reference fingerprints with various distortion fields sampled from the statistical model of distortion fields. The proposed distortion rectification algorithm by performs well by performing matching experiments on various databases. The proposed algorithm can improve recognition rate of distorted fingerprints evidently. SYSTEM ARCHITECTURE:
- 6. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS: System : Pentium IV 2.4 GHz. Hard Disk : 40 GB. Floppy Drive : 1.44 Mb. Monitor : 15 VGA Colour. Mouse : Logitech. Ram : 512 Mb. SOFTWARE REQUIREMENTS: Operating system : Windows XP/7. Coding Language : C#.net Tool : Visual Studio 2010 Database : SQL SERVER 2008 REFERENCE: Xuanbin Si, Student Member, IEEE, Jianjiang Feng, Member, IEEE, Jie Zhou, Senior Member, IEEE, and Yuxuan Luo, “Detection and Rectification of Distorted Fingerprints”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 37, NO. 3, MARCH 2015.