Fingerprint Classification Research Articles

Low‐quality fingerprint classification using deep neural network

Fingerprint recognition systems mainly use minutiae points information. As shown in many previous research works, fingerprint images do not always have good quality to be used by automatic fingerprint recognition systems. To tackle this challenge, in this work, the authors are focusing on very low-quality fingerprint images, which contain several well-known distortions such as dryness, wetness, physical damage, presence of dots, and blurriness. They develop an efficient, with high accuracy, deep neural network algorithm, which recognises such low-quality fingerprints. The experimental results have been obtained from the real low-quality fingerprint database, and the achieved results show the high performance and robustness of the introduced deep network technique. The VGG16-based deep network achieves the highest performance of 93% for dry and the lowest performance of 84% for blurred fingerprint classes.

Research on partial fingerprint recognition algorithm based on deep learning

Fingerprint recognition technology is widely used as a kind of powerful and effective authentication method on various mobile devices. However, most mobile devices use small-area fingerprint scanners, and these fingerprint scanners can only obtain a part of the user’s fingerprint information. Besides, traditional fingerprint recognition algorithms excessively rely on the details of fingerprints, and their recognition performance has great limitations in mobile devices which can only get partial fingerprint images due to fingerprint scanners. This paper proposes a partial fingerprint recognition algorithm based on deep learning for the recognition of partial fingerprint images. It can improve the structure of convolutional neural networks, use two kinds of loss functions for network training and feature extraction and finally improve the recognition performance of partial fingerprint images. The experimental results show that the fingerprint recognition algorithm in this paper has a better performance than the existing fingerprint recognition algorithm based on deep learning on the problem of partial fingerprint classification and fingerprint recognition in the public dataset NIST-DB4 and self-built dataset NCUT-FR.

Fingerprint Classification using KMCG Algorithm under Varying Window and Codebook Sizes

Fingerprint Classification using KMCG Algorithm under Varying Window and Codebook Sizes

Performance Analysis of Fingerprint Based Image Enhancement and Minutiae Extraction

Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. In this work we have propose a method for fingerprint image enhancement. Using histogram equalization over filtering and then minutia are calculated. The results achieved are compared with those obtained through some other methods. The Results show some improvement in the minutiae extraction in terms of quantity.

Comparative Analysis of Fingerprint Classification Algorithms- A review

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Improved bounds on 2-frameproof codes with length 4

Frameproof codes (FPCs) are widely studied as they are classic fingerprinting codes that can protect copyrighted materials. The main interests are construction methods and bounds of the number of codewords of FPCs for a fixed length when the alphabet size approaches infinity. In this paper, we focus on the upper bound of the size of FPCs when the fixed length is 4 and the strength is 2. We obtain an upper bound $$2q^2-2q+7$$ on the size of a q-ary 2-FPC of length 4 for any positive integer $$q> 48$$ . The best previously well known bound of this type of FPCs is $$2q^2-2$$ , which is due to Blackburn (SIAM J Discret Math 16:499–510, 2003). Our new upper bound improves the previous upper bound and it is not very far from the current best lower bound $$2q^2-4q+3$$ obtained from the explicit construction due to Chee and Zhang (IEEE Trans Inf Theory 58:5449–5453, 2012).

Image compression based on SVD for BoVW model in fingerprint classification

Image compression based on SVD for BoVW model in fingerprint classification

Multiple Criteria Decision Analysis Based Overlapped Latent Fingerprint Recognition System Using fuzzy Sets

Latent fingerprints have attracted considerable attention from researchers in the fields of forensics and law enforcement applications. Public demand for these applications may be the driving force behind further progress in biometrics research. Although great effort has been taken to devise algorithms for overlapped latent fingerprint classification system, there are still many challenging problems involved in fingerprint classification systems. Most of the fingerprint-based applications will prolong with fingerprint recognition because of its proven performance, the existence of large databases and the availability of the fingerprint devices with minimum cost. There are various issues that need to be addressed to develop fingerprint classification system. In this connection, there are some designing challenges such as nonlinear distortion, low-quality image, segmentation, sensor noise, skin conditions, overlapping, inter-class similarity, intra-class variations and template ageing. In crime scenes, the latent images can be overlapped with some background images or more number of fingerprint images from same person or different person. An overlapped fingerprint image should be processed for fingerprint classification. This proposed recognition system suggests multiple criteria decision analysis technique to assess the overlapped latent fingerprints. The proposed multiple criteria such as first-order, second-order and third-order features are used to classify the overlapped latent fingerprints. The proposed system designs the novel classification system for overlapped latent fingerprint images using ANFIS classifier. Extensive experiments are performed on the simultaneous latent fingerprint databases, and National Institute of Standards and Technology-Special Database 27, Fingerprint Verification Competition 2006 Database1-A and Database2-A databases. The planned work enables accurate and fast data retrieval by using one-to-N fingerprint classification for overlapped latent images. The experimental results are highly promising, and they outperform the existing systems in classifying overlapped images. The performance of adaptive neuro fuzzy inference system classifier is evaluated by applying the k-fold cross-validation technique. The outcome of the work shows that the overlapped fingerprint is classified in a successful manner, and the results are compared with Bayes, SVM and MLP classifiers. The obtained results show that the proposed system achieves a better classification rate of 90.66% with 5 s, 86.66% with 12 s compared to the existing system.

An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor.

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

Обзор современных моделей представления дактилоскопических изображений

Personal fingerprint identification is the most common method of biometric identification. In this paper we presented a review of modern fingerprint image representation models and fingerprint matching methods based on these models. Fingerprint image may be represented as a set of local features, a set of global features, a set of papillary lines and in the form of topological object which includes combination of features. The main advantages and disadvantages of common fingerprint representations were defined. Also authors proposed a classification of fingerprint represented models by the level of integrity of their description. Each level of this model describe fingerprint image using some characteristic (local features, global features, direction and density of ridges, direction field et al.) or as a solid topological object at the highest model level. Each level of the model can be used independently or with combination of other models. It allows expanding and supplementing fingerprint image description. The review of models and identification methods based on wide range of patents, scientific articles and program registration certificates for the past few years, which confirms the relevance of the problem and this research.

A STUDY ON FINGERPRINT HASH CODE GENERATION BASED ON MD5 ALGORITHM AND FREEMAN CHAIN CODE

The drastic changes in mobile and wireless based technologies and increasing number of applications and users demanded high-security concern, which leads to research on biometrics with a purpose to increase the security aspects and to minimize security threats. The current global mindset toward terrorism has influenced people and their governments to take some special actions and be extra proactive in protection or security problems. Fingerprint image and identification technology have been in life for hundreds of years. Archaeologists have exposed proof suggesting that interest in fingerprints dates to prehistory. But the modern study reveals that fingerprint is not so secured like secured passwords which consist of alphanumeric characters, number and special characters. Fingerprints are left at crime places, on materials or at the door which is usually class of latent fingerprints. We cannot keep fingerprint as secure like rigid passwords. In this paper, we discuss fingerprint image Hash code generation based on the MD5 Algorithm and Freeman Chain code calculated on the binary image. Freeman chain code extracts all possible boundaries for an image and which gives starting x and y positions as x0 and y0. Hashcode alone not sufficient for Verification or Authentication purpose, but can work along with Multifactor security model or it is half secured. To implement Hash code generation we use MATLAB2015a. This study shows how fingerprints Hash code uniquely identifies a user or acts as index-key or identity-key.

Research and implementation of integrated fingerprint classification system based on backtracking feedback

Fingerprint classification has always been an important research direction in the field of intelligent recognition. Based on the method of fingerprint classifier integration, the backtracking feedback mechanism is introduced, and a fingerprint classification system with high recognition rate is designed. Through the use of 1000 fingerprint images in the fingerprint library to test, The system show the recognition results due to the current Kalle Karu, anli K.jain design of a variety of fingerprint recognition system. Through a series of experimental comparisons, it is proved that the fingerprint classification recognition system with the feedback mechanism has better ability of fingerprint recognition, and greatly reduces the error rate of system recognition.

A novel fingerprint classification system using BPNN with local binary pattern and weighted PCA

Fingerprint classification is an important indexing scheme to reduce fingerprint matching time. In this paper, a novel approach to classify fingerprint images is proposed. It involves four main parts: denoising, feature extraction, dimensionality reduction and classification. Initially, the fingerprint is denoised using undecimated wavelet transform. Then short time Fourier transform (STFT) is used to enhance the denoised fingerprints. A set of local binary pattern (LBP) features are extracted to overcome the difficulty associated with singular point detection. To reduce the dimensionality of the feature space, quick reduct (QR), principal component analysis (PCA) and weighted PCA have been investigated. Finally, the fingerprint images are classified using back propagation neural network (BPNN). In this research, experiments have been conducted on real-time fingerprint images collected from 150 subjects and also on the NIST-4 dataset. The proposed method has been compared with support vector machine (SVM), K-nearest neighbor (K-NN), and multi-layer perceptron (MLP).

Classification of Latent Fingerprints Using Neural Networks

Classification of Latent Fingerprints Using Neural Networks

A novel fingerprint classification system using BPNN with local binary pattern and weighted PCA

A novel fingerprint classification system using BPNN with local binary pattern and weighted PCA

Indoor Positioning System with Pedestrian Dead Reckoning and BLE Inverse Fingerprinting

Since the adoption of Bluetooth Low Energy (BLE) in the Bluetooth standard in 2010, BLE beacons are emerging as one of the most viable solutions for indoor localization due to its power efficient architecture, short scan duration, low cost chipset, and wide adoption in the devices. The existing indoor positioning systems based on BLE beacons employ the classical fingerprinting (FP) technique where user terminals collect signals from the beacons and do most of localization computations, requiring significant power consumption on user devices. However, constant power consumption on limited battery life of a mobile device can be problematic when it comes to supporting server-oriented tracking applications. To address this issue, we have proposed a new fingerprinting technique called inverse fingerprinting (Inv-FP), which is a server side BLE fingerprint system where most of the positioning computations are done by BLE sniffers and servers, thus minimizing the computation overhead of user devices. However, the absolute positioning schemes such as FP and Inv-FP do not use the current position estimate to determine the next position. This leads to discontiguous, irregular route prediction especially when the positioning accuracy is low, since it does not reflect the continuity of the position change according to the movement of the user. In contrast, a relative positioning scheme such as Pedestrian Dead Reckoning (PDR) determines the current position based on the previous position, reflecting the continuity of the position change but it cannot estimate the current position without the initial position. In this paper, we implement both FP and Inv-FP and evaluate their performance in small and large-scale testbeds. We analyze various characteristics of Inv-FP in comparison with the classical beacon based FP, and demonstrate that Inv-FP can match the performance of FP but with minimal power consumption on user devices. In addition, we propose a new localization algorithm that can combine Inv-FP with PDR. By integrating PDR with Inv-FP, we show that localization error can be reduced by reflecting the advantages of each method.

A Study on Fingerprint Hash Code Generation Using Euclidean Distance for Identifying a User

Biometrics innovation has ended up being a precise and proficient response to the security issue. Biometrics is a developing field of research as of late and has been dedicated to the distinguishing proof or authentication of people utilizing one or multiple inherent physical or behavioural characteristics. The unique fingerprint traits of a man are exceptionally exact and are special to a person. Authentication frameworks in light of unique fingerprints have demonstrated to create low false acceptance rate and false rejection rate, alongside other favourable circumstances like simple and easy usage strategy. But the modern study reveals that fingerprint is not so secured like secured passwords which consist of alphanumeric characters, number and special characters. Fingerprints are left at crime places, on materials or at the door which is usually class of latent fingerprints. We cannot keep fingerprint as secure like rigid passwords. In this paper, we discuss fingerprint image Hash code generation based on the Euclidean distance calculated on the binary image. Euclidean distance on a binary image is the distance from every pixel to the nearest neighbour pixel which is having bit value one. Hashcode alone not sufficient for Verification or Authentication purpose, but can work along with Multifactor security model or it is half secured. To implement Hash code generation we use MATLAB2015a. This study shows how fingerprints Hash code uniquely identifies a user or acts as index-key or identity-key.

A Study on Fingerprint Hash Code Generation Using Euclidean Distance for Identifying a User

Biometrics innovation has ended up being a precise and proficient response to the security issue. Biometrics is a developing field of research as of late and has been dedicated to the distinguishing proof or authentication of people utilizing one or multiple inherent physical or behavioural characteristics. The unique fingerprint traits of a man are exceptionally exact and are special to a person. Authentication frameworks in light of unique fingerprints have demonstrated to create low false acceptance rate and false rejection rate, alongside other favourable circumstances like simple and easy usage strategy. But the modern study reveals that fingerprint is not so secured like secured passwords which consist of alphanumeric characters, number and special characters. Fingerprints are left at crime places, on materials or at the door which is usually class of latent fingerprints. We cannot keep fingerprint as secure like rigid passwords. In this paper, we discuss fingerprint image Hash code generation based on the Euclidean distance calculated on the binary image. Euclidean distance on a binary image is the distance from every pixel to the nearest neighbour pixel which is having bit value one. Hashcode alone not sufficient for Verification or Authentication purpose, but can work along with Multifactor security model or it is half secured. To implement Hash code generation we use MATLAB2015a. This study shows how fingerprints Hash code uniquely identifies a user or acts as index-key or identity-key.

ANALYSIS OF GALTON-HENRY CLASSIFICATION METHOD FOR FINGERPRINT DATABASE FVC 2002 AND 2004

According to Galton-Henry, fingerprint classification varies into arch, tented arch, left loop, right loop, whorl, and twin loop schemes. The approach is depended on the existence of a core and delta of each fingerprint. If this method is implemented for all fingerprint benchmark databases, then less than 41% of the fingerprint is being classified, by 37.19% for FVC 2002 and 40.31% for FVC 2004. Therefore, in this research, three requirements are needed to improve the classification result of the fingerprint, i.e. core point and its number, ridge frequency and ridge direction, and tented arch as additional requirement. This approach improves the result which is only 5.94% and 1.56% that is unclassified for FVC 2002 and 2004 consecutively. Then, to evaluate the time taken in executing the algorithm, this research does the evaluation by offering two possible conditions of the input of the fingerprint. The first type is without the fingerprint classification, while the second type is with the classification step along the algorithm process. The latter type requires an additional step namely RoI selection process to select a desired area of the fingerprint.