Fingerprint Image Enhancement Research Articles

IoT Based Smart Attendance Management System

Abstract: This research work has application for attendance system of employer's and students in general. The system will facilitate institutions! organization to make attendance individual in time along with data information thumb impression will be taken as a signature for the system entry. Main design and challenge in this system is the design of database architecture and its business logic. I. AIMS AND OBJECTIVE The aim of this system is to implement in C#.net set of reliable techniques for fingerprint image enhancement and minutiae extraction. The performance of these techniques will be evaluated on a fingerprint data set. In combination with these development techniques, statistical experiments can then be performed on the fingerprint data set. The results from these experiments can be used to help us better understand what is involved in determining the statistical uniqueness of fingerprint minutiae. The main aim that this system would test whether attendance by fingerprint is enough for identification. It is expected that the work in this system will reach the stage of being able to fully test hypothesis. II. BACKGROUND/CONTEXT Fingerprints are the oldest form of biometric identification. Modem fingerprint-based identification is used in forensic science, and in biometric systems such as civilian identification devices. Keywords: the fingerprint data, statistical uniqueness of fingerprint minutiae.

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

Many societal entities now have more excellent standards for the efficacy and dependability of identification systems due to the ongoing advancement of computer technology. Traditional identification methods, such as keys and smart cards, have been supplanted by biometric systems in highly secure environments. This research presents a smart computational method for automatically authenticating fingerprints for identity (ID) verification and personal identification. Compared to more traditional machine learning algorithms, the results from applying Deep learning (DL) in areas like computer vision, image identification, robotics, and voice processing have generally been positive. Due to their capacity to analyse big data size and deal with fluctuations in biometric data (such as ageing or expression problems), DL has been heavily used by the artificial intelligence research community. Several biometric systems have succeeded with automatic feature extraction employing deep learning approaches like Convolutional Neural Networks (CNNs). In this research, we provide a biometric process that uses convolutional neural networks. This work introduces a deep learning-based biometric identification system that uses Monte Carlo Dropout (MC Dropout). Combining these two systems makes the authentication process more secure and dependable. Fingerprint image enhancement techniques involve the application of Gabor filters and structure-adaptive anisotropic filters, which have proven to be effective in enhancing the clarity and distinctiveness of fingerprint patterns. To improve the efficiency of deep learning models, this work proposes the Inception-Augmentation GAN (IAGAN) model for data augmentation. The study adds to security development by integrating novel biometric identification and authentication approaches with cutting-edge neural network technology. In this research, we provide a new activation function to speed up the convergence of deep neural networks. The results of 99.6% on Gabor filters and 99.8% on the structure-adaptive anisotropic filter with GACNN with MCD show that deep neural networks can excel over competing approaches with enough training data.

Leachate xenobiotics electrocatalytic degradation and simultaneous carbon quantum dots synthesis for anti-counterfeiting applications

The leachate treatment and the management of sludge after its treatment is a big challenge due to its many toxins and pollutants. The novelty of the current work resides in our attempt to address both difficulties by treating the leachate with electrofenton using low-cost stainless steel electrodes, followed by converting the sludge generated into quantum dots suited for anti-counterfeiting applications. On the electrofenton treatment of leachate, at the optimized conditions, the percentage removal of total organic carbon (TOC), UV254, chemical oxygen demand (COD), and NH3-N was found to be 79 %, 93 %, 90 %, and 90 % respectively with less energy consumption of 0.26 KWh/KgCOD. The gas chromatography-mass spectroscopy (GC–MS), 3D fluorescence spectroscopy matrix, and UV–Vis analysis confirmed the removal of xenobiotics and dissolved organic matter (DOM). The carbon quantum dots (S-CQDs) synthesized from sludge were characterized for X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier transforms infrared spectroscopy (FTIR), PL intensity (Photoluminescence), and UV–Vis. The S-CQDs are very promising in qualitative fingerprint image enhancement in terms of overall pattern, dots, and ridges and are well suited as an alternate, efficient, green anticounterfeiting material. Thus, highly polluted leachate was efficiently treated and sludge was converted into useful carbon dots with the anti-counterfeiting application.

Fingerprint image enhancement using multiple filters.

Biometrics is the measurement of an individual's distinctive physical and behavioral characteristics. In comparison to traditional token-based or knowledge-based forms of identification, biometrics such as fingerprints, are more reliable. Fingerprint images recorded digitally can be affected by scanner noise, incorrect finger pressure, condition of the finger's skin (wet, dry, or abraded), or physical material it is scanned from. Image enhancement algorithms applied to fingerprint images remove noise elements while retaining relevant structures (ridges, valleys) and help in the detection of fingerprint features (minutiae). Amongst the most common image enhancement filters is the Gabor filter, however, given their restricted maximum bandwidth as well as limited range of spectral information, it falls short. We put forward a novel method of fingerprint image enhancement using a combination of a diffusion-coherence filter and a 2D log-Gabor filter. The log-Gabor overcomes the limitations of the Gabor filter while Coherence Diffusion mitigates noise elements within fingerprint images. Implementation is done on the FVC image database and assessed via visual comparison with coherence diffusion used disjointedly and with the Gabor filter.

Fingerprint classification combined with Gabor filter and convolutional neural network

A fingerprint is an impression left by the friction ridges of a human finger. A fingerprint classification system groups fingerprint according to their characteristics and therefore helps to match a fingerprint against an extensive database of fingerprints. The Henry classification system is widely used among fingerprint classification systems. Some researchers have used traditional machine learning or deep learning for fingerprint classification. Nevertheless, traditional algorithms cannot extract the depth features of the fingerprint, and most deep learning algorithms lack fingerprint image enhancement. So, this paper combined the Gabor Filter and Convolutional Neural Network to extract fingerprint features. The model has two channels, one is a Deep Convolutional Neural Network (DCNN), and the other is a Shallow Convolutional Neural Network (SCNN). The DCNN consists of a neural network with eight layers, which can extract deep features of the fingerprint. The SCNN consists of Gabor Filter and a neural network with two layers that can extract features from clear fingerprint images. This paper uses NIST Special Database 4 for experiments. Experimental results show that the model proposed in this paper has achieved 91.4% accuracy. Compared with other algorithms, this model has higher accuracy than others. It shows that combined with the Gabor Filter and Convolutional Neural Network can better extract the ridge features of fingerprint images.

Fingerprint Recognition Using Deep Learning - A Review

There have been efforts to address the problems with fingerprint identification systems that require physical contact by creating contactless fingerprint identification systems. Numerous studies on various aspects of contactless fingerprint processing, including the use of deep learning in various algorithmic frameworks, classical image processing, and the machine-learning pipeline, have been published. It was demonstrated that the deep learning-based solutions were more accurate than the alternatives. This effort was driven by a desire to provide a thorough assessment of these successes and their identified limitations. This study examined three approaches to contactless fingerprint recognition: (i) methods for capturing images of the fingerprint, (ii) traditional preprocessing techniques for enhancing fingerprint images for recognition tasks, and (iii) deep learning. (i) taking a picture of your finger, and (ii) using conventional image processing to get the picture ready for recognition. In total, eight research papers were found to meet both the inclusion and exclusion criteria. Based on this review's findings, we discussed the potential benefits of deep learning methods for biometrics and the challenges that still need to be overcome before these methods can be used in practical biometric settings.

A functional enhancement on scarred fingerprint using sigmoid filtering

Fingerprint has been widely used in biometric applications. Numerous established researches on image enhancement techniques have been done to improve the quality of fingerprint images. However, the production of low-quality images due to the presence of scars remains a challenge in biometrics. The scars damage the fingerprint minutiae information due to broken ridges and they reduce the accuracy of identification. This research developed an image enhancement approach to improve the quality of scarred fingerprint images to generate accurate minutiae extraction. To achieve the aim, the scarred image was improved by removing noise using a new filter, Median Sigmoid (MS), and the corrected ridges were reconstructed using ridges structure enhancement algorithm. This was done to enhance the broken ridges structure. MS filter is a combination of median filter and modified sigmoid function that improves the image contrast and simultaneously removes noise in the fingerprint image. Following that, the filtered image was used in the ridges structure enhancement process. To identify true minutiae, the broken ridges structure in the filtered image needed to be accurately verified. In the ridges structure reconstruction process, an algorithm was enhanced to identify the best value of Sigma parameter (σ) used in the Gaussian Low-pass filter to generate a better orientation image. The image is important to reconstruct the corrupted fingerprint ridges structure. The evaluation for the proposed approach used the National Institute of Standards and Technology Special Database 14, and the results showed a 37% improvement of the quality index in comparison to approaches found in related research. The findings of the evaluation showed that the proposed enhancement approach produced a better minutiae extraction result and this is very significant in the field of fingerprint image enhancement.

An Improved Fingerprint-based Document Image Retrieval using Multi-resolution Histogram of Oriented Gradient Features

Recently most of the documents are authenticated by using a latent fingerprint impression. Examples of such documents are property registration, banking transactions, insurance documents, etc. The fingerprint-based document retrieval (FPDIR) has emerged to provide an easier way of accessing, browsing, or searching such document images. This paper proposes efficient fingerprint-based document image retrieval by employing multi-resolution Histogram of Oriented Gradient (HOG) features. The preprocessing technique presented in this paper employs a combination of top-hat and bottom-hat filtering operations to enhance the detected fingerprint image. Multi-resolution HOG features are constructed from horizontal, vertical and diagonal directional components of the enhanced fingerprint image. Finally, a standardized Euclidean distance metric is used as a tool for matching, ranking and retrieval of the document images. The proposed system is assessed by experimenting with a dataset of 1200 images. The precision and recall results obtained using the proposed research work have given an improvement of 8% to 14% in retrieval performance compared to earlier methods.

Application of Machine Learning in Fingerprint Image Enhancement and Recognition: A Review

Biometric characteristics helps to recognize an individual among others. Each individual has a unique biometric feature. So, an automated system is designed to recognize an individual. In today’s growing AI development, biometric recognition is applied in many security systems. One of oldest and widely used authentic biometric methodology is fingerprint recognition. Many fingerprint recognition algorithms are designed and developed in order to reduce error rate and to improve accuracy. In this paper, a comprehensive review is presented on various techniques used for fingerprint recognition system along with their performance and their limitations. The purpose of this paper is to review various recent work on the fingerprint recognition system, to explain step by step the steps for recognizing fingerprints, and to provide summaries of the fingerprint databases with functionality

Singular point region enhancement of fingerprint image based on symmetric phase consistency

Aiming at the problem that Separable Gabor filter can destroy the ridge structure of fingerprint adjacent singular points, and the effect of directional Fourier filter in repairing fingerprint ridges is not obvious, a method for singular point region

Cycle-consistent generative adversarial neural networks based low quality fingerprint enhancement

Distortions such as dryness, wetness, blurriness, physical damages and presence of dots in fingerprints are a detriment to a good analysis of them. Even though fingerprint image enhancement is possible through physical solutions such as removing excess grace on the fingerprint or recapturing the fingerprint after some time, these solutions are usually not user-friendly and time consuming. In some cases, the enhancements may not be possible if the cause of the distortion is permanent. In this paper, we are proposing an unpaired image-to-image translation using cycle-consistent adversarial networks for translating images from distorted domain to undistorted domain, namely, dry to not-dry, wet to not-wet, dotted to not-dotted, damaged to not-damaged, blurred to not-blurred. We use a database of low quality fingerprint images containing 11541 samples with dryness, wetness, blurriness, damages and dotted distortions. The database has been prepared by real data from VISA application centres and have been provided for this research by GEYCE Biometrics. For the evaluation of the proposed enhancement technique, we use VGG16 based convolutional neural network to assess the percentage of enhanced fingerprint images which are labelled correctly as undistorted. The proposed quality enhancement technique has achieved the maximum quality improvement for wetness fingerprints in which 94% of the enhanced wet fingerprints were detected as undistorted.

Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction

Fingerprints are the one of the most important means in the forensics as a means of identification of the criminals owning to the uniqueness and the distinct features in them. Fingerprint identification is considered as an important means for the identification of the people around the globe. Minutiae are the details present in the human fingerprints which are used as a means of identification and verification. Minutiae are the distinctive points which can be used for the effective reconstruction of the fingerprint image. However, there was a limitation that was considered. The minutiae points are completely not enough for reconstruction of the image. Many spurious minutiae are not included and the results for the latent fingerprints are not as accurate as they are for the normal data sets. In this paper, a novel technique has been proposed which considers the minutiae density and the orientation field direction for the reconstruction of the fingerprint. Two public domain databases Fingerprint verification competition 2002 (FVC2002) and fingerprint verification competition 2004 (FVC2004) have been used for the experimental results and to validate the suggested methods for the fingerprint reconstruction and enhancement.

Multi-task CNN for restoring corrupted fingerprint images

Fingerprint image enhancement is one of the fundamental modules in an automated fingerprint recognition system (AFRS). While the performance of AFRS advances with sophisticated fingerprint matching algorithms, poor fingerprint image quality remains a major issue to achieve accurate fingerprint recognition. In this paper, we present a multi-task convolutional neural network (CNN) based method to recover fingerprint ridge structures from corrupted fingerprint images. By learning from the noises and corruptions caused by various undesirable conditions of finger and sensor, the proposed CNN model consists of two streams that reconstruct the fingerprint image and orientation field simultaneously. The enhanced fingerprint is further refined using the orientation field information. Moreover, we create a deliberately corrupted fingerprint image dataset associated with ground truth images to facilitate the supervised learning of the proposed CNN model. Experimental results show significant improvement on both image quality and fingerprint matching accuracy after applying the proposed fingerprint image enhancement technique to several well-known fingerprint datasets.

Fingerprint Enhancement Based on Tensor of Wavelet Subbands for Classification

Fingerprint image enhancement is a key aspect of an automated fingerprint identification system. This paper describes an effective algorithm based on a novel lighting compensation scheme. The scheme involves the use of adaptive higher-order singular value decomposition on a tensor of wavelet subbands of a fingerprint (AHTWF) image to enhance the quality of the image. The algorithm consists of three stages. The first stage is the decomposition of an input fingerprint image of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2M\times 2N$ </tex-math></inline-formula> into four subbands at the first level by applying a two-dimensional discrete wavelet transform. In the second stage, we construct a tensor in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {R}^{M\times N\times 4}$ </tex-math></inline-formula> space. The tensor contains four wavelet subbands that serve as four frontal planes. Furthermore, the tensor is decomposed through higher-order singular value decomposition to separate the fingerprint’s wavelet subbands into detailed individual components. In the third stage, a compensated image is produced by adaptively obtaining the compensation coefficient for each frontal plane of the tensor-based on the reference Gaussian template. The experimental results indicated that the quality of the AHTWF image was higher than that of the original image. The proposed algorithm not only improves the clarity and continuity of ridge structures but also removes the background and blurred regions of a fingerprint image. Therefore, this algorithm can achieve higher fingerprint classification accuracy than related methods can.

Sensitive development of latent fingerprints using Rhodamine B-diatomaceous earth composites and principle of efficient image enhancement behind their fluorescence characteristics

Latent fingerprints, by definition, are not directly visible traces that usually found at crime scenes and need physical or chemical treatment before image capture and fingerprint identification. Research of novel powders for latent fingerprint development and establishment of efficient methods for fingerprint image enhancement have always been key focuses in forensic science and related fields involving chemistry, materials science and biology. Relying on physical adsorption between cationic fluorophores and porous silica, Rhodamine B-diatomaceous earth composites have been prepared as novel fluorescent fingerprint powders. A luminescence mode for latent fingerprint development and image enhancement that matches the fluorescence characteristics of these composites has been theoretically explained and a fingerprint image capture and processing system designed based on it has been successfully constructed. In the luminescence mode, the first and second level information of a latent fingerprint on the surface of an object showing either background interference or not can be precisely visualized and captured. This entire approach is demonstrated to be promising for scenarios of practical application.

Enhancement and Identification of Finger Print Image

In the technological era, it is essential to identify personal details of a person for various reasons. One of the most trustable commonly used methods for identifying a person is physical traits called biometrics of the person. This biometric information may be a person’s iris, finger print, voice of face. Among these factors finger print is most commonly used one. Nowadays even mobile phone uses fingerprint identification. In forensic and law & order department consider finger print matching as an important factor to identify a person. But recognizing fingerprint from poor quality images is still a very complex problem.To overcome this problem it is essential to enhance the details of contactless fingerprint.This can be done by a proper image enhancement algorithm like thresholding and binarization. Fingerprint recognition can be done by using feature extraction method.

A Fingerprint Image Enhancement Algorithm Based on Stretching Transfer Function

In this paper, a fingerprint image enhancement algorithm based on the stretching transfer function is proposed. The Otsu method is used to generate the threshold, which divides the fingerprint image grayscale roughly into two parts. The width of grayscale distribution can be controlled by adjusting the parameter of the stretching transfer function. The contrast of the input fingerprint image will be enhanced by the function, highlight image layers and make the ridge and valley lines clearer. The function could also retain the fingerprint image information in transformation process, thus lays a solid foundation for the accuracy and reliability of the fingerprint characteristic value extraction in the next step. The treatment effect of the algorithm is obviously better than that of histogram equalization algorithm; the superiority also lies in the smaller amount of calculation and the faster processing speed which effectively avoids manual intervention. It can be used for the occasion with limited computing resources and high real-time requirements. Besides, the ability to control the contrast of output image by only changing a single parameter provides users with regulations and choices in biometric.

Fingerprint Image Thinning by applying Zhang Suen Algorithm on Enhanced Fingerprint Image

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.

FINGERPRINT IMAGE ENHANCEMENT USING FILTERING TECHNIQUES.

22May 2019 FINGERPRINT IMAGE ENHANCEMENT USING FILTERING TECHNIQUES. Ganchimeg.G and Leopold.H. Senior Lecturer, Mongolian University of Science and Technology. Head of Center for Digital Safety & Security, Austrian Institute of Technology.

Two-stage quality adaptive fingerprint image enhancement using Fuzzy C-means clustering based fingerprint quality analysis

Fingerprint recognition techniques are dependent on the quality of fingerprint images. An efficient enhancement algorithm improves the performance of recognition algorithms for poor quality images. Performance improvement of the recognition algorithms will be more if the enhancement process is adaptive to the fingerprint qualities (wet, dry or normal). In this paper, a quality adaptive fingerprint enhancement algorithm is proposed. The proposed fingerprint quality assessment (FQA) algorithm assigns the appropriate quality class of dry, wet, normal dry, normal wet, and good quality using Fuzzy C-means clustering technique to each fingerprint image. It considers seven features namely, mean, moisture, variance, uniformity, contrast, ridge valley area uniformity (RVAU), and ridge valley uniformity (RVU) to cluster the fingerprint images into suitable quality class. Fingerprint images of each quality class undergo through a two-stage fingerprint quality enhancement (FQE) process. In the first stage, a quality adaptive preprocessing (QAP) method is used to preprocess the fingerprint images. Next, fingerprint images are enhanced with Gabor, short-term Fourier transform (STFT), and oriented diffusion filtering (ODF) based enhancement techniques in the second stage. Experimental evaluations are performed on a quality driven database of FVC 2004. Results show that the performance improvement of 1.54% to 50.62% for NBIS matcher and 1.66% to 8.95% for VeriFinger matcher are achieved while the QAP based approaches are used in comparison to the current state-of-the-art enhancement techniques. In addition, the experimentation is also performed on FVC 2002 database to validate the robustness and efficacy of the proposed method.