Distorted Fingerprint Verification System

Fingerprint verification is one of the most reliable personal identification methods. Fingerprint matching is affected by non-linear distortion introduced in fingerprint impression during the image acquisition process. This non-linear deformation changes both the position and orientation of minutiae. The proposed system operates in three stages: alignment based fingerprint matching, fuzzy clustering and classifier framework. First, an enhanced input fingerprint image has been aligned with the template fingerprint image and matching score is computed. To improve the performance of the system, a fuzzy clustering based on distance and density has been used to cluster the feature set obtained from the fingerprint matcher. Finally a classifier framework has been developed and found that cost sensitive classifier produces better results. The system has been evaluated on fingerprint database and the experimental result shows that system produces a verification rate of 96%. This system plays an important role in forensic and civilian applications.Biometric, Fingerprints, Distortion, Fuzzy Clustering, Cost Sensitive Classifier

Features based text similarity detection

As the Internet help us cross cultural border by providing different information, plagiarism issue is bound to arise. As a result, plagiarism detection becomes more demanding in overcoming this issue. Different plagiarism detection tools have been developed based on various detection techniques. Nowadays, fingerprint matching technique plays an important role in those detection tools. However, in handling some large content articles, there are some weaknesses in fingerprint matching technique especially in space and time consumption issue. In this paper, we propose a new approach to detect plagiarism which integrates the use of fingerprint matching technique with four key features to assist in the detection process. These proposed features are capable to choose the main point or key sentence in the articles to be compared. Those selected sentence will be undergo the fingerprint matching process in order to detect the similarity between the sentences. Hence, time and space usage for the comparison process is reduced without affecting the effectiveness of the plagiarism detection

Random Sample Consensus Algorithm with Enhanced Latency for Fingerprint Matching

This publication describes a random sample consensus algorithm with enhanced latency for fingerprint matching. A fingerprint image can be represented by a fingerprint image template which includes key points (X and Y locations) and feature vectors. Key points correspond to various minutiae points of the fingerprint image. Feature vectors are rotationally invariant encodings of image blocks centered around key points. Fingerprint matching is done by comparing feature vectors from a verify fingerprint image template to feature vectors from an enrolled fingerprint image template to generate matching key point pairs. A geometric transformation between the verify fingerprint image template and the enrolled fingerprint image template is inferred by a random sample and consensus process of matching key point pairs. The geometric transformation between two matching fingerprint image templates is mainly a rigid two-dimensional (2D) transformation with a translation vector, a rotation matrix, and minimal stretch. Rather than comparing every matching key point pair to infer the geometric transformation, the disclosed fingerprint matching algorithm implements a stretch ratio check. For any two key points, the stretch ratio is the distance between the two key points from the verify fingerprint image template divided by the distance between matching key points from the enrolled fingerprint image template. If the stretch ratio falls outside an acceptable range of stretch ratios, the fingerprint matching algorithm skips the set of matching key point pairs of that stretch ratio when inferring the geometric transformation. By so doing, the fingerprint matching algorithm improves matching speed and makes matching and non-matching latencies consistent