Optical spatial-frequency correlation system for fingerprint recognition

This paper deals with the fingerprint classification and recognition system, which consists of extracting and matching of minutiae from the input image. The necessity for security in fields such as improving airport security, strengthening the national borders, in travel documents, in preventing ID theft has brought the need to develop an able and efficient method for correct classification of personnel authentication. Fingerprints are the first biometric science used widely for the validation and verifications of entry into specific task, which is more reliable, efficient and accurate. Despite finger print recognition being reliable it has disadvantages such as very low recognition rate, low accuracy rate, total time of recovery and data insufficiency. To address above problem a novel data mining technique, neuro nearest neighbour based fingerprint classification and recognition, is introduced which boosts the classification rate. The proposed method consists of different stages such as image enhancement, line detector based feature extraction, neural network classification using Learning vector quantization and Back propagation networks. The proposed system is trained and tested on Fingerprint Database obtained from the University of Bologna Italy, which consists of 900 samples. The exact image is recognized precisely from the classified database rather than the original set of database using Crisp K-nearest neighbour algorithm that increases recognition accuracy and reduction in time

A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from the given image with faces within a database. It is also described as a Biometric Artificial Intelligence-based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. While initially a form of computer application, it has seen wider uses in recent times on mobile platforms and in other forms of technology, such as robotics. It is typically used as access control in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. Although the accuracy of facial recognition system as biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless and non-invasive process. Recently, it has also become popular as a commercial identification and marketing tool. Other applications include advanced human-computer interaction, video surveillance, automatic indexing of images and video database, among others. The use of facial recognition has recently become a very debatable subject and has been criticized more and more because it was considered an unethical tool used to spy on the public. The reason for such criticism is, however, largely due to the lack of information and regulation of this technology. Used proportionally and responsibly, facial recognition can and should be beneficial. It has the capacity to do much more to increase security in the future-from street crime to airport security. Armed war crime has dominated UK titles throughout the year. Recent statistics indicate that the number of people who benefited from the emergency assistance due to armed attacks increased by almost 40% compared to the two years ago, while the number of children under 18 years of age with stab wounds is increasing by 86 % in only four years. Face recognition has become a normal activity in many airports around the world. Many people today have a so-called biometric passport that allows them to go faster to the gate without having to be controlled. The facial recognition used in this way has significantly reduced waiting times for passport control but also has the ability to increase security in and around airports. Face recognition thus allows officers to identify an individual more quickly and accurately than the human eye. While some critics may worry about technology-related confidentiality issues, airports have shown that the use of facial recognition has improved security as well as speeding up processes such as check-in and in the future, even procedures boarding. When used correctly and proportionally, facial recognition can help protect the public and improve national security on multiple fronts. Similarly, advanced technology can recognize a person seen on CCTV security systems at the crime scene, justifying a person's stop and search. The ability to check in real time whether a person is on the list of criminal investigations adds an added advantage to the decision-making process before stopping and searching, thus lowering the probability of discrimination.

ABSTRACTRecent advances in information and communication technology (ICT) have pushed education systems into the online paradigm. Although fingerprint recognition has received considerable attention from researchers, the concept of creating online or distance education systems for fingerprint recognition systems is a valid problem that requires further investigation. This article addresses the problem and presents a web platform for online education of a fingerprint recognition system. This study focused on the technical design and implementation of the web platform solely as a proof of concept to illustrate the possibility of creating distance education for fingerprint recognition. The developed platform offers students a way to gain knowledge about the full range of fingerprint recognition system technology. The significance of using a web platform stems from the need to supplement students’ theoretical knowledge with practical hands-on experience with information security in general and with fingerprint recognition in particular. Demonstrations such as fingerprint image enhancement and fingerprint feature extraction are included in the current platform prototype. Plots of fingerprint recognition performance curves such as false match rate (FMR) and false non-match rate (FNMR) as functions of the matching threshold curve, the receiver operating characteristic (ROC) curve, and the detection error trade-off (DET) curve are plotted as part of the platform. From a technical viewpoint, the platform was successfully implemented; however, its educational impact needs further evaluation.

There are various types of applications for fingerprint recognition which is used for different purposes. Fingerprint is one of the challenging pattern Recognition problem. The Fingerprint Recognition system is divided into four stages. First is Acquisition stage to capture the fingerprint image, The second is Pre-processing stage to enhancement, binarization, thinning fingerprint image. The Third stage is Feature Extraction Stage to extract the feature from the thinning image by use minutiae extractor methods to extract ridge ending and ridge bifurcation from thinning. The fourth stage is matching(Identification, Verification) to match two minutiae points by using minutiae matcher method in which similarity and distance measure are used. The algorithm is tested accurately and reliably by using fingerprint images from different databases. In this paper the fingerprint databases used are FVC2000 and FVC2002 Databases, we see that, the FVC2002 database perform better results compare with FVC2000 database. The recognition system evaluate with two factor FAR and FRR, In this system the result of FAR is 0.0154 and FRR is 0.0137 with Accuracy equal to 98.55%.

Typical fingerprint recognition systems are comprised of a spoof detection module and a subsequent recognition module, running one after the other. In this study, we reformulate the workings of a typical fingerprint recognition system. We show that both spoof detection and fingerprint recognition are correlated tasks, and therefore, rather than performing these two tasks separately, a joint model capable of performing both spoof detection and matching simultaneously can be used without compromising the accuracy of either task. We demonstrate the capability of our joint model to obtain an authentication accuracy (1:1 matching) of TAR \(=\) 100% @ FAR \(=\) 0.1% on the FVC 2006 DB2A dataset while achieving a spoof detection ACE of 1.44% on the LivDet 2015 dataset, both maintaining the performance of stand-alone methods. In practice, this reduces the time and memory requirements of the fingerprint recognition system by 50 and 40%, respectively; a significant advantage for recognition systems running on resource constrained devices and communication channels.

Fingerprint recognition technology is integral to biometric security systems, providing secure and reliable identification through unique human fingerprint patterns. However, challenges such as low contrast, high intra-class variability, and partial fingerprints often compromise the efficiency and accuracy of traditional recognition systems. This research addresses these challenges by employing advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs), to enhance fingerprint recognition performance. We propose a methodological approach that leverages state-of-the-art CNN architectures tailored to capture intricate fingerprint details. The study utilizes the Sokoto Coventry Fingerprint Dataset (SOCOFing), which includes diverse fingerprint types and synthetic alterations to evaluate model performance under realistic conditions. Through a comparative analysis of various CNN configurations, we assessed the models based on efficiency and accuracy, using metrics such as accuracy, precision, recall, and F1-score. Our experimental results demonstrate significant improvements in fingerprint recognition capabilities. The optimized CNN model achieved an accuracy of 98.61%, a precision of 97.12%, a recall of 97.46%, and an F1-score of 97.29%. These results validate the effectiveness of CNNs in handling complex biometric data and underscore their potential to enhance the reliability and security of fingerprint recognition systems. The study concludes that deep learning, through the use of CNNs, offers a powerful solution to the limitations of traditional fingerprint recognition techniques. This will pave the way for more sophisticated and accurate biometric security systems in practical applications. The research findings contribute to ongoing advancements in neural network architectures, enhancing their applicability in increasingly automated and data-driven security environments.

최근 보안 시스템 중에서 지문인식을 이용한 보안 시스템은 다른 보안 시스템에 비해 유일성과 편의성 등의 장점을 가짐으로써 많은 사람들이 관심을 갖는 분야이다. 지문인식 시스템에 있어서 가장 중요한 사항은 실제 지문과 영상을 통해 얻어진 지문간의 정합에서의 정확성과 지문 인식을 위해 사용하는 영상처리 알고리즘들을 신속하게 처리하는 데 있다. 기존의 지문인식 시스템은 특징 추출을 위해 사용하는 알고리즘들의 처리 시간을 줄이기 위해 전체 처리과정 중 일부 과정들을 생략함으로써 처리시간을 단축한다. 하지만 이 방식은 처리시간을 단축시킬 수 있는 반면 특징 추출에 대한 정확도가 떨어진다. 따라서 본 논문에서는 특징 추출에 대한 정확도를 높이기 위해 전체 처리 과정을 사용하면서 동시에 처리시간도 단축시킬 수 있는 다중 접근 메모리 시스템을 이용한 지문인식 특징 추출 알고리즘을 구현하였고, 구현된 시스템을 사용하였을때 다중접근 메모리 시스템과 시리얼 프로세서의 결과에 대해 correlation을 이용한 검증을 통해 제안된 방법의 신뢰도를 확인하였으며, 시리얼 프로세서에 비해 MAMS-PP64를 이용한 방법은 수행시간에서 약 1.56배 향상되었음을 확인하였다. Among the recent security systems, security system with fingerprint recognition gets many people's interests through the strengths such as exclusiveness, convenience, etc, in comparison with other security systems. The most important matters for fingerprint recognition system are reliability of matching between the fingerprint in database and user's fingerprint and rapid process of image processing algorithms used for fingerprint recognition. The existing fingerprint recognition system reduces the processing time by removing some processes in the feature extraction algorithms but has weakness of a reliability. This paper realizes the fingerprint recognition algorithm using MAMS(Multi-Access Memory System) for both the rapid processing time and the reliability in feature extraction and matching accuracy. Reliability of this process is verified by the correlation between serial processor's results and MAMS-PP64's results. The performance of the method using MAMS-PP64 is 1.56 times faster than compared serial processor.

Biometrics is a technology which identifies a person based on his physiology or behavioral characteristics. Fingerprint identification and recognition is a biometrics method that has been widely used in various applications because of its reliability and accuracy in the process of recognizing and verifying a person's identity. The main purpose of this paper is to develop a fingerprint identification and recognition system. The system consists of three main parts, image acquisition, processing and identification and recognition. Fingerprint images are acquired and stored in the database in the image acquisition stage. These images are then enhanced in the image processing stage by performing gray level enhancement, spatial filtering, image sharpening, edge detection, segmentation, and thinning processes. After the image has been processed, it is fed into the backpropagation neural network as input in order to train the network. After training, the neural network is ready to perform the identification and recognition operations (matching process). A neural network has been successfully developed to identify and recognize the core part of fingerprint images.

Typical fingerprint recognition systems are comprised of a spoof detection module and a subsequent recognition module, running one after the other. In this paper, we reformulate the workings of a typical fingerprint recognition system. In particular, we posit that both spoof detection and fingerprint recognition are correlated tasks. Therefore, rather than performing the two tasks separately, we propose a joint model for spoof detection and matching <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> to simultaneously perform both tasks without compromising the accuracy of either task. We demonstrate the capability of our joint model to obtain an authentication accuracy (1:1 matching) of TAR = 100% @ FAR = 0.1% on the FVC 2006 DB2A dataset while achieving a spoof detection ACE of 1.44% on the LiveDet 2015 dataset, both maintaining the performance of stand-alone methods. In practice, this reduces the time and memory requirements of the fingerprint recognition system by 50% and 40%, respectively; a significant advantage for recognition systems running on resource-constrained devices and communication channels.

In general, most fingerprint recognition systems are based on the minutiae feature points. When matching two fingerprint images, the goal in most recognition systems is to find the optimal transformation model that aligns their feature points in order to find among them the number of matched or aligned points and then generate a matching score. A major problem in feature extraction stage is that when the fingerprint image is of a poor quality due to skin conditions and sensor noise, that leads to many broken ridges in the image caused by cutline. In this case, the extraction of minutiae leads to a lot of spurious points and the performance of the system will degrade. Usually, image enhancement techniques are applied as preprocessing step to overcome this problem. In this work, we propose to use corner points on fingerprint ridges as new features in addition to the ridges minutiae in order to improve the recognition performance. Every ridge is decomposed into several straight edges (SEs). A straight edge is defined as a straight link of ridge points. On a ridge, the head of the first straight edge and the tail of the last one are two minutia and the intersections of the SEs are the ridge corners. Thus, we propose to use a ridge as primitive rather than individual points for matching. This primitive is a structure consisting of groups of both feature points which are minutiae and corners belonging to the same ridge. Based on this primitive, an intelligent matching technique is introduced using sets of feature points on the same primitive. As a result, the recognition performance is increased since it is based on ridge primitive matching rather than individual minutiae matching. Finally, our experimental results compared with those obtained by other well-known techniques in the literature demonstrate the effectiveness and efficiency of our proposed algorithm.

Fingerprint recognition is very stable, reliable, and oldest biometric authentication technology that has been widely used in various civilian and government applications. Like other authentication technologies, fingerprint recognition is not totally free from spoofing. A fingerprint recognition system can be spoofed by placing artificially generated fingerprints of enrolled user at the sensor that are made up of materials like gelatine, silicon, wax or play-doh etc. Liveness detection is a technique to enhance the security of such systems by detecting physiological life signs in fingerprint samples to differentiate a real fingerprint from an artificial one. A live fingerprint image has the property to produce gray level variations along ridges due to presence of sweat pores, perspiration and skin quality (dry, wet) whereas a fake fingerprint has uniform gray levels that can give a fair idea about the liveness in input sample. In the proposed work, two fingerprints are captured by scanner at different time points (0 sec and 2 sec). In order to find their gray level variations, a gray level co-occurrence matrix of each fingerprint image is determined. After that, contrast value is computed from each matrix. If the difference in their contrast value is greater than threshold, then the input fingerprint sample is considered as live otherwise it is considered as fake. Liveness detection solution presented in this work helps to avoid direct attacks at fingerprint sensor that result in very secure and precise fingerprint detection. Keywords: Biometrics, Fingerprint detection, Liveness Detection, Spoofing

Biometric-based system is one of the systems for identification/recognition and verification. There are many types of biometric technology, one of them is fingerprint recognition system. Fingerprint, like other biometric feature is unique, means that it will always different with its kind. One of the problems in fingerprint recognition systems is how to extract the characteristics/feature of a person's fingerprint. This article present fingerprint matching application as part of recognition, using simple minutiae point extraction algorithm implemented it into hardware-based Artificial Neural Network device. The Cogniblox is an ANN device from CogniMem that comes with 4 CM1K chip, able to store up to 4096 neurons in the device. MATLAB environment is used for developing the fingerprint recognition application. The designed system is then undergo a performance evaluation by recognizing a total of 80 dataset of fingerprint, consist of 10 different fingerprint with 8 impression each, which stored into the neurons beforehand. The performance evaluation resulted in a False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) are presented.

Fingerprint recognition schemas are widely used in our daily life, such as Door Security, Identification, and Phone Verification. However, the existing problem is that fingerprint recognition systems are easily tricked by fake fingerprints for collaboration. Therefore, designing a fingerprint liveness detection module in fingerprint recognition systems is necessary. To solve the above problem and discriminate true fingerprint from fake ones, a novel software-based liveness detection approach using uniform local binary pattern (ULBP) in spatial pyramid is applied to recognize fingerprint liveness in this paper. Firstly, preprocessing operation for each fingerprint is necessary. Then, to solve image rotation and scale invariance, three-layer spatial pyramids of fingerprints are introduced in this paper. Next, texture information for three layers spatial pyramids is described by using uniform local binary pattern to extract features of given fingerprints. The accuracy of our proposed method has been compared with several state-of-the-art methods in fingerprint liveness detection. Experiments based on standard databases, taken from Liveness Detection Competition 2013 composed of four different fingerprint sensors, have been carried out. Finally, classifier model based on extracted features is trained using SVM classifier. Experimental results present that our proposed method can achieve high recognition accuracy compared with other methods.

IARPA launches challenge to improve automated ‘nail to nail’ capture

Image-based and minutiae-based are two major methods of fingerprint recognition. In this work, we presented an image-based fingerprint recognition method by using wavelet transformation and this method is efficient even for low quality fingerprint. The features extraction of the proposed method differing with previous wavelet methods is based on the blocks of enhanced region of interest (ROI). The alignment is required to build ROI including location the reference point and rotation alignment. Fingerprint matching was performed on simply Euclidian distance of feature vector extracted from wavelet domain. These features consist of mean energy, standard deviation and Shannon entropy for the purpose of making these features more discriminative. The good recognition accuracy was achieved on the FVC2002 database.