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

Algorithm for Fingerprint Verification System

Extraction of minutiae based features from good quality fingerprint images is more effective for fingerprint recognition in comparison with features from low quality fingerprint. In this paper, a new technique for fingerprint feature extraction based on ridge pattern is proposed. Robust features are extracted from fingerprint image notwithstanding the quality of the image. The variation within different person fingerprint is established using centre of gravity of the fingerprint image as the reference point for effective classification. Similarity measure in term of Euclidean distance is compute for test fingerprint image

Pembuatan Prototype Alat Identifikasi Golongan Darah pada Manusia Bebasis Pola Sidik Jari Menggunakan Scanner Optik

In this 21st century where the need of blood increases rapidly, efficiency, speed and accuracy of blood group identification process to be one essential thing to do. In this tool is designed and manufactured a humans blood groups identification device based on fingerprint patterns using optical scanners. Broadly, the purpose of this tool is the ABO blood group (blood group A, B, AB and O) identification process which is practically and efficient and introduced to public the latest technology appropriate to the medical electronics field, especially in the field of ABO blood group identification. Fingerprint pattern of the 5 right hand finger users are captured and processed for identification of blood type. Manufacture of hardware in the form control box consisting of minimum system, LCD, keypad and an UareU 4000B optical scanner. The ABO blood group identification process using Microsoft Visual Basic 6.0 and for the database is used Microsoft Access. Pattern recognition using Euclidean Distance and ABO blood group identification process using K-Mean cluster method and Euclidean Distance. Based on the results of tests conducted on 68 respondents, the results has obtained fingerprint pattern recognition in three fingerprint patterns (Loop, Whorl and Arch) and the ABO blood group identification had blood group A, B, AB and O detected. Keywords : ABO, Database, Fingerprint Microcontroller, Optical Silico

Error propagation in pattern recognition systems: Impact of quality on fingerprint categorization

The aspect of quality in pattern classification has recently been explored in the context of biometric identification and authentication systems. The results presented in the literature indicate that incorporating information about quality of the input pattern leads to improved classification performance. The quality itself, however, can be defined in a number of ways, and its role in the various stages of pattern classification is often ambiguous or ad hoc. In this dissertation a more systematic approach to the incorporation of localized quality metrics into the pattern recognition process is developed for the specific task of fingerprint categorization. Quality is defined not as an intrinsic property of the image, but rather in terms of a set of defects introduced to it. A number of fingerprint images have been examined and the important quality defects have been identified and modeled in a mathematically tractable way. The models are flexible and can be used to generate synthetic images that can facilitate algorithm development and large scale, less time consuming performance testing. The effect of quality defects on various stages of the fingerprint recognition process are examined both analytically and empirically. For these defect models, it is shown that the uncertainty of parameter estimates, i.e. extracted fingerprint features, is the key quantity that can be calculated and propagated forward through the stages of the fingerprint classification process. Modified image processing techniques that explicitly utilize local quality metrics in the extraction of features useful in fingerprint classification, such as ridge orientation flow field, are presented and their performance is investigated

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

Inkjet-Printed Carbon Nanotubes for Fabricating a Spoof Fingerprint on Paper.

A spoof fingerprint was fabricated on paper and applied for a spoofing attack to unlock a smartphone on which a capacitive array of sensors had been embedded with a fingerprint recognition algorithm. Using an inkjet printer with an ink made of carbon nanotubes (CNTs), we printed a spoof fingerprint having an electrical and geometric pattern of ridges and furrows comparable to that of the real fingerprint. With this printed spoof fingerprint, we were able to unlock a smartphone successfully; this was due to the good quality of the printed CNT material, which provided electrical conductivities and structural patterns similar to those of the real fingerprint. This result confirms that inkjet-printing CNTs to fabricate a spoof fingerprint on paper is an easy, simple spoofing route from the real fingerprint and suggests a new method for outputting the physical ridges and furrows on a two-dimensional plane

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