Implementasi Alignment Point Pattern pada Sistem Pengenalan Sidik Jari Menggunakan Template Matching

Fingerprints is one of biometric identification system. This is because fingerprints have unique and different pattern in every human, so identification using fingerprints can no longer be doubted. But, manual fingerprint recognition by human hard to apply because of the complex pattern on it. Therefore, an accurate fingerprint matching system is needed. There are 3 steps needed for fingerprint recognition system, namely image enhancement, feature extraction, and matching. In this study, crossing number method is used as a minutiae extraction process and template matching is used for matching. We also add alignment point pattern  process added, which are ridge translation and  rotation to increase system performance. The system provide a performance of 18,54% with a matching process without alignment point pattern, and give performance of 67,40% by adding alignment point pattern process

3D minutiae extraction in 3D fingerprint scans.

Traditionally, fingerprint image acquisition was based on contact. However the conventional touch-based fingerprint acquisition introduces some problems such as distortions and deformations to the fingerprint image. The most recent technology for fingerprint acquisition is touchless or 3D live scans introducing higher quality fingerprint scans. However, there is a need to develop new algorithms to match 3D fingerprints. In this dissertation, a novel methodology is proposed to extract minutiae in the 3D fingerprint scans. The output can be used for 3D fingerprint matching. The proposed method is based on curvature analysis of the surface. The method used to extract minutiae includes the following steps: smoothing; computing the principal curvature; ridges and ravines detection and tracing; cleaning and connecting ridges and ravines; and minutiae detection. First, the ridges and ravines are detected using curvature tensors. Then, ridges and ravines are traced. Post-processing is performed to obtain clean and connected ridges and ravines based on fingerprint pattern. Finally, minutiae are detected using a graph theory concept. A quality map is also introduced for 3D fingerprint scans. Since a degraded area may occur during the scanning process, especially at the edge of the fingerprint, it is critical to be able to determine these areas. Spurious minutiae can be filtered out after applying the quality map. The algorithm is applied to the 3D fingerprint database and the result is very encouraging. To the best of our knowledge, this is the first minutiae extraction methodology proposed for 3D fingerprint scans

Digital Camera Identification Using Neural Network Algorithm And Pattern Noise In Imaging Sensors

In forensic investigation of criminal cases like child pornography, image forgery, identity theft, steganography, movie piracy, insurance claims, and other cases of scientific frauds, some of the most significant challenges may be to detect the origin of an image or the photographing camera, detect forged images or hidden messages in images from retrieved digital evidence. There has been much interest in developing camera fingerprints for the forensic task of digital camera identification; that is, to be able to tie an image to it\u27s photographing camera with high certainty or good assurance metrics, specially when the camera is not present in the crime scene. Inspired by the existing approaches of camera fingerprint forensics, this paper explores a novel approach for camera identification, based on PRNU noise fingerprint, using Artificial Neural Network (ANN) algorithm. While statistical algorithms produce probabilistic inferences based on statistical problem data, artificial neural network algorithm learns features about the problem from training data. Based on correctness of feature representations and complex mathematical processing on the training data, the neural network is able to learn or approximate any non-linear distribution very easily. As it trains on different examples, it\u27s generalization performance on new inputs improves. In currently proposed work, first the reference fingerprint and test fingerprint are estimated based on a simple kernel based processing algorithm for PRNU coefficient estimation. Then an artificial neural network is set up in C programming language for PRNU pattern recognition based on the estimated feature values from the reference pattern data. The network is presented with training inputs and desired outputs, and based on formulated assumptions and hypothesis described in later sections, the expectation is that the ANN will be able to recognize PRNU fingerprint in images taken by the same camera whose fingerprint the ANN got trained on. A low Mean Square Error (MSE) during ANN training and testing is an indication that the ANN could report with high confidence, a match between the camera fingerprint pattern and the pattern in test image. Multilayer Perceptron (MLP) ANN with single hidden layer is proved to be a universal non-linear function approximator and can be applied to solve any complex non-linear problem. Current approach uses back propagation MLP ANN algorithm for fingerprint detection or camera identification

PERFORMANCE ENHANCEMENT OF BACKPROPAGATIONALGORITHM USING MOMENTUM AND LEARNINGRATEWITH A CASE STUDY ON FINGERPRINT RECOGNITION

Artificial Neural Network (ANN) is a branch of artificial intelligence theory that has been used in various applications such as pattern recognition. The advantages of ANN as a system is the ability to imitate human thoughts in computational intelligence such as pattern recognition. ANN is useful to do modelling prediction, error detection and control systems with artificial intelligence approaches and computational design. There are 3 methods that commonly used in ANN heuristic rule, delta-delta rule, and delta- bar-delta rule. Delta-bar-delta rule that use by backpropagation method is the best algorithm to solve the problem input to the network [5]. By applying learning rate [3] in backpropagation algorithm, learning process will be more stable and faster in finding the optimal in the delta (stepsize) by reducing error for optimal solution. Shao and Zheng [4] apply momentum in backpropagation algorithm and the result shows that the error sequence is monotonously decreased during the training procedure and the algorithm is weakly convergent, the gradient of error sequence converges to zero as the training iteration goes on. Fingerprint is one of Biometric identity measurement using pattern recognition that is important to determine the accuracy of personal identification. Fingerprints had strong nature of unchangeable over time and each person is different from the others from one person to another. Conventional biometric fingerprint technology sometimes is inaccurate because the fingerprint position is alterated in scanner tools. This disadvantage can be minimize using ANN method with Backpropagation algorithm. Fingerprint recognition using standard backpropagation shows 66,91% average accuracy and 225 seconds of average training time. The accuracy increases by adding momentum and learning rate with gradual value in Backpropagation algorithm. Average accuracy of 80,9% can be achieved using combination of momentum and learningrate, and 144 seconds average training time. Keywords: Neural Networks, fingerprint patterns, Backpropagation, momentum, learningrat

FINGERPRINT RECOGNITION SYSTEM

This project is to design a fingerprint recognition system for security purposes. It will also explore and suggest some solution to the improvement to the existing fingerprint system. Security system that uses a pin code or access card can be easily misused or mishandled. A pin code can be cracked using some hacker software while an access card can easily be stolen or misplaced. Thus, these security methods are very vulnerable to hackers and criminals. Instead, a fingerprint is unique to every person and due to the fact that no two people have the same fingerprint pattern, it makes the fingerprint a very good resource in a security system. The aim of this project is to focus on the concept and methodology of the fingerprint recognition system. By grasping the concept and method of the fingerprint recognition flow, a prototype is developed that will compare an input fingerprint with its predefined template. The system should be able to compare and decide if the input fingerprint is the same as the predefined template. The output of the first stage is a preprocessing stage. There are two stages involved in preprocessing which is the image enhancement and image skeletonization. Fourier transfonn and histogram equalization is utilized to enhance the low quality image to a better image so that the feature extraction process will run smoothly. The second stage of the project is to define the orientation, ROI extraction and minutia extraction. The matching sequence and the angle orientation problem were resolved

Fingerprint recognition system to verify the identity of a person using an online database

Our B.Tech project emphasize on the current techniques for the fingerprint recognition. Human fingerprint exhibit some certain details marked on it. We categorized it as minutiae, which can be used as a unique identity of a person if we recognize it in a well manner. The main aim of this project is to design a complete system and an indigenous design model for fingerprint verification from an online database using minutiae matching technique. So, in order to have a good quality minutiae extraction the fingerprint image is first pre-processed by image enhancement which includes histogram equalization, Fast Fourier Transform and binerization and then segmentation is done to get the effective area of the fingerprint followed by minutiae extraction which includes ridge thinning and minutiae marking and then we have a post-processing operation which includes removal of H-breaks, isolated points and false minutiae. Now, we go for a final treatment which is ‘minutiae matching’, in minutiae matching we match the post-processed fingerprint image with the online database. For all these operations, we develop an alignment based matching algorithm which is for minutiae matching. This algorithm has a specialty that it itself finds the correspondences between input minutiae and the stored template minutiae pattern and there is no resorting to exhaustive search. We can then evaluate the performance of the system on a database by taking fingerprints of different people

Implementasi Deep Learning Berbasis Tensorflow Untuk Pengenalan Sidik Jari

The fingerprint recognition system is widely used in biometrics for various purposes in recent years. Fingerprint recognition is used because it has a complex pattern that can recognize a person and is the identity of every human being. Fingerprints are also widely used as verification and identification. Problems encountered in this research is the difficult to classify objects one of them on fingerprints. In this study the authors use deep learning using the method of Convolutional Neural Network (CNN) to overcome these problems. CNN is used to perform machine learning process on computer. Stages on CNN are data input, preprocessing, training process. The implementation of CNN used in this research is tensorflow library by using python programming language. The dataset used originated from a fingerprint verification competition website in 2004 using optical sensor type “V300” by CrossMatch and in it there were 80 fingerprint images. The training process uses 24x24 pixel data and performs the test by comparing the number of epoch and learning rate so it is known that if the greater the number of epoch and smaller the learning rate the better the accuracy of the training obtained. In this research, the accuracy level of training is 100%

FLAG : the fault-line analytic graph and fingerprint classification

Fingerprints can be classified into millions of groups by quantitative measurements of their new representations - Fault-Line Analytic Graphs (FLAG), which describe the relationship between ridge flows and singular points. This new model is highly mathematical, therefore, human interpretation can be reduced to a minimum and the time of identification can be significantly reduced. There are some well known features on fingerprints such as singular points, cores and deltas, which are global features which characterize the fingerprint pattern class, and minutiae which are the local features which characterize an individual fingerprint image. Singular points are more important than minutiae when classifying fingerprints because the geometric relationship among the singular points decide the type of fingerprints. When the number of fingerprint records becomes large, the current methods need to compare a large number of fingerprint candidates to identify a given fingerprint. This is the result of having a few synthetic types to classify a database with millions of fingerprints. It has been difficult to enlarge the minter of classification groups because there was no computational method to systematically describe the geometric relationship among singular points and ridge flows. In order to define a more efficient classification method, this dissertation also provides a systematic approach to detect singular points with almost pinpoint precision of 2x2 pixels using efficient algorithms

An FPGA-based Embedded System For Fingerprint Matching Using Phase Only Correlation Algorithm

none5There is an increasing interest in inexpensive and reliable personal identification in many emerging civilian, commercial and financial applications. Traditional systems such as passwords, PINs, Badges, Smart Cards and Tokens may either be stolen or easy to guess but also to forget, in same cases they may be lost by the user who carries them; all this can lead to identified. Fingerprint-based identification is one of the most used biometric techniques in automated systems for personal identification and it is becoming socially acceptable and cost-effective, since a fingerprint is univocally related to a particular individual. Typical fingerprint identification methods employ feature-based image matching, where minutiae points in the ridge lines (i.e., ridge endings and bifurcations) are identified. Unfortunately this approach is highly influenced by fingertip surface condition. Fingerprint recognition is a complex pattern recognition problem. The efforts to make automatic the matching process based on digital representation of fingerprints, led to the development of Automatic Fingerprint Identification Systems (AFIS). Typically, there are millions of fingerprint records in a database which needs to be entirely searched for a match, to establish the identity of the individual. In order to provide a reasonable response time for each query, it will be better to develop special hardware solutions to implement matching and/or classification algorithms in a really efficient way. In this work we realised a system able to outperform modern PCs in recognising and classifying fingerprints and based on FPGA technology.Il lavoro si è classificato al II posto nell'Altera Contest 2009 Innovate Italy, gara annuale indetta da Altera tra progetti di team di giovani studenti universitari su tutto il territorio nazionale.Giovanni Danese; Mauro Giachero; Francesco Leporati; Giulia Matrone; Nelson NazzicariDanese, Giovanni; Giachero, Mauro; Leporati, Francesco; Matrone, Giulia; Nelson, Nazzicar

Detection of Singular Points from Fingerprint Images Using an Innovative Algorithm

Fingerprint scrutiny is typically based on the location and pattern of detected singular points in the images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. In this report, there is an innovative algorithm for singular points detection. After an initial detection using the conventional Poincare Index method, a so-called DORIVAC feature is used to remove spurious singular points. Then, the optimal combination of singular points is selected to minimize the difference between the original orientation field and the model-based orientation field reconstructed using the singular points. A core-delta relation is used as a global constraint for the final selection of singular points. Keywords: Orientation field, Poincare´ Index, Singular points, topological structur