Enhanced Ridge Direction for the Estimation of Fingerprint Orientation Fields

An accurate estimation of fingerprint orientation fields is an important step in the fingerprint classification process. Gradient-based approaches are often used  for estimating orientation fields of ridge structures but this method is susceptible to noise. Enhancement of ridge direction improves the structure of orientation fields and increases the number of correct features thereby conducing the overall performance of the classification process. In this paper, we propose an algorithm to improve orientation field structures using variance of gradient. That algorithm have two steps; firstly, estimation of fingerprint orientation fields using gradient-based method, and finally, enhancement of ridge direction using minimum variance of the cross center block direction. We have used standard fingerprint database NIST-DB14 for testing of proposed algorithm to verify the degree of efficiency of algorithm. The experiment results suggest that our enhanced algorithm achieves visibly better noise resistance with other methods

ARM7 based Smart ATM Access System

ARM7 Based Smart ATM System is designed to add more security to the ATM systems by using biometric, OTP and Accelerometer sensor. In our proposed system, Bankers will collect the customer’s fingerprints and mobile number while opening the account then only customers can access the ATM machine. The primary step of this project is to verify currently scanned finger print with the fingerprint which is registered in the bank. If it finds as a valid then ATM machine, will ask 4 digit pin which is fixed. If the 4 digit code matches with entered pin then system will automatically generates another different 4 digit code i.e. OTP. And that code will be message to the customer registered mobile number. Here customer has to enter this code again. After entering OTP, System will check whether entered code is valid or not. And if it is valid, the customer is allowed for further accessing. Also Accelerometer sensor is used in order to provide security for the ATM machine. DOI: 10.17762/ijritcc2321-8169.15059

Log-Gabor Orientation with Run-Length Code based Fingerprint Feature Extraction Approach

This paper aims to design and implement Log-Gabor filtering with Run-length Code based feature Extraction technique. Since minutiae extraction is an essential and core process of fingerprint Identification and Authentication systems, the minutiae features are enhanced in each orientation using Log-Gabor filter and features are extracted using the proposed method. Frequency domain is derived using FFT and they are enhanced by Log-Gabor filter for each orientation. In our method six orientations are considered; binarization, thinning are also followed. Fingerprint features are extracted using proposed method which possesses labeling and Run-length Coding technique. Our method is tested with the benchmark Databases and real time images and the results show the better performance and lower error rate

Novel Feature Extraction Methodology with Evaluation in Artificial Neural Networks Based Fingerprint Recognition System

Fingerprint recognition is one of the most common biometric recognition systems that includes feature extraction and decision modules. In this work, these modules are achieved via artificial neural networks and image processing operations. The aim of the work is to define a new method that requires less computational load and storage capacity, can be an alternative to existing methods, has high fault tolerance, convenient for fraud measures, and is suitable for development. In order to extract the feature points called minutia points of each fingerprint sample, Multilayer Perceptron algorithm is used. Furthermore, the center of the fingerprint is also determined using an improved orientation map. The proposed method gives approximate position information of minutiae points with respect to the core point using a fairly simple, orientation map-based method that provides ease of operation, but with the use of artificial neurons with high fault tolerance, this method has been turned to an advantage. After feature extraction, General Regression Neural Network is used for identification. The system algorithm is evaluated in UPEK and FVC2000 database. The accuracies without rejection of bad images for the database are 95.57% and 91.38% for UPEK and FVC2000 respectively

Automatic fingerprint classification scheme using template matching with new set of singular point-based features

Fingerprint classification is a technique used to assign fingerprints into five established classes namely Whorl, Left loop, Right loop, Arch and Tented Arch based on their ridge structures and singular points’ trait. Although some progresses have been made thus far to improve accuracy rates, problem arises from ambiguous fingerprints is far from over, especially in large intra-class and small inter-class variations. Poor quality images including blur, dry, wet, low-contrast, cut, scarred and smudgy, are equally challenging. Thus, this thesis proposes a new classification technique based on template matching using fingerprint salient features as a matching tool. Basically, the methodology covers five main phases: enhancement, segmentation, orientation field estimation, singular point detection and classification. In the first phase, it begins with greyscale normalization, followed by histogram equalization, binarization, skeletonization and ends with image fusion, which eventually produces high quality images with clear ridge flows. Then, at the beginning of the second phase, the image is partitioned into 16x16 pixels blocks - for each block, local threshold is calculated using its mean, variance and coherence. This threshold is then used to extract a foreground. Later, the foreground is enhanced using a newly developed filling-in-the-gap process. As for the third phase, a new mask called Epicycloid filter is applied on the foreground to create true-angle orientation fields. They are then grouped together to form four distinct homogenous regions using a region growing technique. In the fourth phase, the homogenous areas are first converted into character-based regions. Next, a set of rules is applied on them to extract singular points. Lastly, at the classification phase, basing on singular points’ occurrence and location along to a symmetric axis, a new set of fingerprint features is created. Subsequently, a set of five templates in which each one of them represents a specific true class is generated. Finally, classification is performed by calculating a similarity between the query fingerprint image and the template images using x2 distance measure. The performance of the current method is evaluated in terms of accuracy using all 27,000 fingerprint images acquired from The National Institute of Standard and Technology (NIST) Special Database 14, which is de facto dataset for development and testing of fingerprint classification systems. The experimental results are very encouraging with accuracy rate of 93.05% that markedly outpaced the renowned researchers’ latest works

FINGERPRINT ENHANCEMENT USING FUZZY LOGIC AND DEEP NEURAL NETWORKS

Department of Computer Science and EngineeringFingerprint recognition analysis is one of the most leading preferred prodigious biometric advancement which has drawn generous consideration in biometrics. In this work, fingerprint Intensification is performed which is defined by Fuzzy logic technique and recognize the matching image with its unique characteristics extracted and classify the features extracted from a fuzzy enhanced image along with three major types of Neural Networks which are Feed Forward Artificial Neural Network, Neural Network, Recurrent Neural Network in order to classify the unique features extracted from a fingerprint image. This work efficiently expresses the results with Fuzzy logic enhancement and Neural Networks classifiers. Its principle goal is to improve the image using Fuzzy and extricate the spurious minutiae detected and classify the different features generated using GLCM and DWT. This work displays a framework of unique finger impression classification based on particular characteristics for extricating different features and three types of Neural Network for classification. Fuzzy technique is used for the fuzzy based image enhancement to urge the clear see of the unique finger impression. Fingerprint Image Intensification is the procedure to enhance the distorted images to encourage the recognizable proof. The motivation behind the work is to enrich the quality of the distorted condition image generated from any fingerprint sensor, as Images can be corrupted due to various conditions and one of the principal issues is the resolution of the fingerprint sensor generating noisy images. High-quality pictures are vital for the exact coordinating of unique finger impression pictures. But unique mark pictures are seldom of idealizing refinement. As it may be corrupted or debased due to varieties of the skin, impression state and condition. In this way, unique finger impression images must be improved before utilized. The idea behind this work fingerprint image intensification process is to improve the quality of distorted and noisy fingerprint images generated from a low-cost fingerprint sensor. Execution of current ???ngerprint acknowledgment frameworks is vigorously in???uenced by the precision of their characteristic???s extraction evaluation. These days, there are more ways to deal with ???ngerprint analysis with worthy outcomes. Issues begin to emerge in low-quality conditions where the dominant part of the conventional strategies dependent on examining the surface of ???ngerprint can't handle this issue so e???ectively as Neural Networks. Fuzzy logic technique is implemented first to remediate the distorted picture and enhance it with the implementation of GLCM and DWT2 algorithm features of an image is extracted, post to which three types of Neural Network Classification is performed to analyze the accuracy of the image generated from the extracted feature parameters and match the test and trained result with the implementation of Neural Networks and classify the outcome results. The three Neural Network used is Artificial Neural Network (ANN), Neural Network (NN), Recurrent Neural Network (RNN). This algorithm works efficiently to identify the fingerprint matching from the predefined trained images from the fuzzy enhanced image generated. Experiments are performed (in MATLAB 2019 student version) to make sure the extraction process should not get the false minutiae and preserve the true extracted features Fuzzy based Image Enhancement method makes sure the feature traits of the image is intensified. Better improvement proves the quality improvement further incrementing the highest accuracy determined in the classification further. This work can be used in a wide area of applications in biometrics as it is a combined work of distorted fingerprints enhancement, false feature removal, true feature extraction, matching of the images for identification purpose and classification using Neural Networks. Experiments show results which are quite promising and gives a direction of the subsequent further analysis in future work.clos

Embedded electronic systems driven by run-time reconfigurable hardware

Abstract This doctoral thesis addresses the design of embedded electronic systems based on run-time reconfigurable hardware technology –available through SRAM-based FPGA/SoC devices– aimed at contributing to enhance the life quality of the human beings. This work does research on the conception of the system architecture and the reconfiguration engine that provides to the FPGA the capability of dynamic partial reconfiguration in order to synthesize, by means of hardware/software co-design, a given application partitioned in processing tasks which are multiplexed in time and space, optimizing thus its physical implementation –silicon area, processing time, complexity, flexibility, functional density, cost and power consumption– in comparison with other alternatives based on static hardware (MCU, DSP, GPU, ASSP, ASIC, etc.). The design flow of such technology is evaluated through the prototyping of several engineering applications (control systems, mathematical coprocessors, complex image processors, etc.), showing a high enough level of maturity for its exploitation in the industry.Resumen Esta tesis doctoral abarca el diseño de sistemas electrónicos embebidos basados en tecnología hardware dinámicamente reconfigurable –disponible a través de dispositivos lógicos programables SRAM FPGA/SoC– que contribuyan a la mejora de la calidad de vida de la sociedad. Se investiga la arquitectura del sistema y del motor de reconfiguración que proporcione a la FPGA la capacidad de reconfiguración dinámica parcial de sus recursos programables, con objeto de sintetizar, mediante codiseño hardware/software, una determinada aplicación particionada en tareas multiplexadas en tiempo y en espacio, optimizando así su implementación física –área de silicio, tiempo de procesado, complejidad, flexibilidad, densidad funcional, coste y potencia disipada– comparada con otras alternativas basadas en hardware estático (MCU, DSP, GPU, ASSP, ASIC, etc.). Se evalúa el flujo de diseño de dicha tecnología a través del prototipado de varias aplicaciones de ingeniería (sistemas de control, coprocesadores aritméticos, procesadores de imagen, etc.), evidenciando un nivel de madurez viable ya para su explotación en la industria.Resum Aquesta tesi doctoral està orientada al disseny de sistemes electrònics empotrats basats en tecnologia hardware dinàmicament reconfigurable –disponible mitjançant dispositius lògics programables SRAM FPGA/SoC– que contribueixin a la millora de la qualitat de vida de la societat. S’investiga l’arquitectura del sistema i del motor de reconfiguració que proporcioni a la FPGA la capacitat de reconfiguració dinàmica parcial dels seus recursos programables, amb l’objectiu de sintetitzar, mitjançant codisseny hardware/software, una determinada aplicació particionada en tasques multiplexades en temps i en espai, optimizant així la seva implementació física –àrea de silici, temps de processat, complexitat, flexibilitat, densitat funcional, cost i potència dissipada– comparada amb altres alternatives basades en hardware estàtic (MCU, DSP, GPU, ASSP, ASIC, etc.). S’evalúa el fluxe de disseny d’aquesta tecnologia a través del prototipat de varies aplicacions d’enginyeria (sistemes de control, coprocessadors aritmètics, processadors d’imatge, etc.), demostrant un nivell de maduresa viable ja per a la seva explotació a la indústria