Fingerprint Image Quality Research Articles

Exploring the synergy of Zn3(OH)2V2O7 2H2O @Betalains as a promising photoluminescent nanocomposite for sustainable optical and electrochemical applications

For the first time, the new approach was utilized to synthesize a novel Zn3(OH)2V2O7 2H2O/Betalains (ZV/Bn) nanocomposite (NCs) through the mediation of natural dye betalains (Bn) extract and a hydrothermal method. With decreased crystallite size (38 nm) and a lower band gap, ZV/Bn NCs are showing a potential candidate with better performances in photoluminescence, nitrite sensing, and corrosion inhibition studies when compared to pure ZV nanoparticles. The optical band gap of ZV/Bn is 2.7 eV, and the Bn is effective in reducing the band gap of ZV by 0.3 eV. ZV/Bn has shown its intense fluorescence properties by emitting a vibrant green emission with a strong emission peak at 535 nm and a subtle peak at 489 nm. The exceptional ability of ZV/Bn fluorescent was significantly employed for the development of latent fingerprint detection, resulting in the high quality of fingerprint images. ZV/Bn-based sensors have proved to be successful due to their low cost, biocompatibility, and high sensitivity towards the detection of the most important bioanalyte, nitrite. The limit of detection (LOD) and the sensitivity of ZV/Bn-based sensor for the detection of nitrite are 17 μM and 7.8E−6 A/M, respectively. Additionally, it shows potential as a corrosion inhibitor. Hence, ZV/Bn NCs emerge as a versatile candidate for optical and electrochemical applications.

Data augmentation-based enhanced fingerprint recognition using deep convolutional generative adversarial network and diffusion models

The progress of fingerprint recognition applications encounters substantial hurdles due to privacy and security concerns, leading to limited fingerprint data availability and stringent data quality requirements. This article endeavors to tackle the challenges of data scarcity and data quality in fingerprint recognition by implementing data augmentation techniques. Specifically, this research employed two state-of-the-art generative models in the domain of deep learning, namely Deep Convolutional Generative Adversarial Network (DCGAN) and the Diffusion model, for fingerprint data augmentation. Generative Adversarial Network (GAN), as a popular generative model, effectively captures the features of sample images and learns the diversity of the sample images, thereby generating realistic and diverse images. DCGAN, as a variant model of traditional GAN, inherits the advantages of GAN while alleviating issues such as blurry images and mode collapse, resulting in improved performance. On the other hand, Diffusion, as one of the most popular generative models in recent years, exhibits outstanding image generation capabilities and surpasses traditional GAN in some image generation tasks. The experimental results demonstrate that both DCGAN and Diffusion can generate clear, high-quality fingerprint images, fulfilling the requirements of fingerprint data augmentation. Furthermore, through the comparison between DCGAN and Diffusion, it is concluded that the quality of fingerprint images generated by DCGAN is superior to the results of Diffusion, and DCGAN exhibits higher efficiency in both training and generating images compared to Diffusion.

Enhancing Fingerprint Forensics: A Comprehensive Study of Gender Classification Based on Advanced Data-Centric AI Approaches and Multi-Database Analysis

Fingerprint analysis has long been a cornerstone in criminal investigations for suspect identification. Beyond this conventional role, recent efforts have aimed to extract additional demographic information from fingerprints, such as gender, age, and nationality. While studies have demonstrated promising accuracy in gender classification based on fingerprints, practical implementation faces challenges, including the often low quality of crime scene fingerprints. This study presents a pioneering comparison of gender classification across diverse datasets, considering variations in fingerprint image quality. We examine the results from four databases, encompassing both public and private sources, employing state-of-the-art Data-Centric AI (DCAI) approaches for enhanced classification. Our findings reveal that a conservative Convolutional Neural Network (CNN)—specifically VGG—proves effective, achieving an accuracy ranging from 70% to 95% based on fingerprint quality. DCAI methods contribute a noteworthy 1–4% improvement. Notably, for partial or low-quality fingerprints, the periphery emerges as a critical determinant of gender classification. This study contributes insights into practical gender classification from fingerprints, emphasizing the significance of the fingerprint periphery. Furthermore, we provide the source code for future research and accessibility in real-world applications.

Quality-Aware Fusion of Multisource Internal and External Fingerprints Under Multisensor Acquisition

Fingerprint is one of the most widely used biometric features. The researches on external fingerprint collected by total internal reflection and the internal fingerprint obtained by optical coherence tomography have been carried out extensively. Studies proved the consistency between them. The external fingerprint is susceptible to the fingertip status, whereas the internal fingerprint has strong anti-interference and anti-spoofing capabilities. They originate from different skin layers and are collected by multiple sensors. Given the corresponding advantages and disadvantages of external and internal fingerprints, their fusion can maximize effective information and improve fingerprint image quality; thus, this fusion is conducive to fingerprint identification. In this study, a fingerprint fusion method based on the quality-aware convolutional-sparsity-based morphological component analysis (CSMCA) is proposed. The proposed method realizes the fusion of multisource and multisensor fingerprints for the first time. Quality indexes, namely, spatial consistency, dryness, and humidity, are selected. Moreover, a simulation-based combination scheme is proposed for the pixel-level quality representation. The quality index is integrated with CSMCA for quality-aware fusion, which retains high-quality components and reduces low-quality areas. The experiments prove the superiority of the proposed method in quality scores and matching performances, indicating that internal fingerprints can amend external fingerprints. The matching experiments with either external or internal fingerprints show that the fused fingerprints can be compatible with the existing fingerprint databases. Our work can provide references and insights into identifying mutilated fingerprints in the future.

Development of an Improved Multi-filtering Matching Model for Fingerprint Recognition

Over the years research done in the area of fingerprint recognition in which the hybrid matching algorithm is one of the most common techniques, though the hybrid algorithm performed well but still faced with the challenge of false minutiae. This study formulated, simulated, and evaluated a multi-filtering fingerprint matching model to develop a multi-filtering matching model for fingerprint recognition. The method employed a multi-filtering model that was formulated using image pre-processing; minutiae feature extraction, post-processing, and cancellation of false minutiae algorithms in the processed images. The model was simulated using Matlab and fingerprint images from the Fingerprint Verification Competition (FVC) 2002 database. The performance of the model was evaluated using the False Acceptance Rate (FAR), False Rejection Rate (FRR), and Error Equal Rate (EER). The results showed that the false minutiae cancellation algorithm considerably reduced the false minutiae points in the thinned images which resulted in the reduction of false acceptance when two different images were tested, and also reduction in false rejection rate when two same images were tested. The match score was below the threshold value of 50 for false acceptance rate and above the threshold value of 50 for the false rejection rate. The error equal rate EER value of 0.076 was recorded. The study concluded that there was a significant reduction in the false minutiae points present in the thinned images and that a high accuracy of fingerprint matching was achieved when the datasets include poor quality fingerprint images.

39‐4: Analysis of Large Area Optical Fingerprint Recognition Technology under OLED Screen

This paper theoretically and experimentally analyzes the influence of different sensor array schemes and optical collimation structures on fingerprint recognition. Considering the process yield, cost and the quality of fingerprint image, we find the appropriate size ranges of microlenses and sensors are 20~30 μm and 35~50 μm respectively, and the bonding process between the optical collimating film and the sensor substrate has little damage to the sensor, which is conducive to mass production.

An Innovative Possibilistic Fingerprint Quality Assessment (PFQA) Filter to Improve the Recognition Rate of a Level-2 AFIS.

In this paper, we propose an innovative approach to improve the performance of an Automatic Fingerprint Identification System (AFIS). The method is based on the design of a Possibilistic Fingerprint Quality Assessment (PFQA) filter where ground truths of fingerprint images of effective and ineffective quality are built by learning. The first approach, QS_I, is based on the AFIS decision for the image without considering its paired image to decide its effectiveness or ineffectiveness. The second approach, QS_PI, is based on the AFIS decision when considering the pair (effective image, ineffective image). The two ground truths (effective/ineffective) are used to design the PFQA filter. PFQA discards the images for which the AFIS does not generate a correct decision. The proposed intervention does not affect how the AFIS works but ensures a selection of the input images, recognizing the most suitable ones to reach the AFIS's highest recognition rate (RR). The performance of PFQA is evaluated on two experimental databases using two conventional AFIS, and a comparison is made with four current fingerprint image quality assessment (IQA) methods. The results show that an AFIS using PFQA can improve its RR by roughly 10% over an AFIS not using an IQA method. However, compared to other fingerprint IQA methods using the same AFIS, the RR improvement is more modest, in a 5-6% range.

Geometric Distortion Correction of Images Received From Biometric Fingerprint Devices

The quality of fingerprint images is very important for recognition and identification in biometric systems. Geometric distortions and different image scales may worsen fingerprint recognition. It is necessary to adjust distortions and scale in biometric finger capture systems. Algorithms for correcting distortions and scale are investigated, analyzed, and developed in this paper. A polynomial model of second order is used as a mathematical model of spatial distortions. To correct the brightness of image points, bicubic interpolation is used.

Design and implementation of pixel‐based adjustable ESD protection circuits for capacitive fingerprint biometric sensors

AbstractThe trade‐off design between ESD endurability and polymer thickness on sensor plate for capacitive fingerprint is emerging in these years since the image quality of fingerprint is vital for biometrics authentication. In this paper, we propose a pixel‐based ESD protection circuitry in parallel to GDPMOS and GGNMOS and an adjustable body terminal in proposed pixel‐based ESD circuit to meet and exceed air discharge ±15 kV without thicker polymer. With the proposed pixel‐based adjustable ESD protection circuit, the maximum air discharge achieved by (18 kV, −17 kV), and 10 out of 10 samples passed +15 kV, and 10 out of 10 samples passed −15 kV. Compared to conventional ground wall fingerprint sensor, the maximum air discharge was stopped by (15 kV, −15 kV), and just merely five out 10 samples passed 15 kV, seven out of 10 samples passed −15 kV. Besides the excellent ESD endurability, the proposed pixel‐based ESD protection circuit achieves air‐discharge 15.5 kV and grayscale count 230 at merely 50 μm thickness of polymer. Compared to the conventionally used ground wall sensor types, it passes only 13 kV and a grayscale count of 210 at a polymer thickness of 50 μm, but fails 15 KV Level 4 of IEC‐61000‐4‐2. Even with polymer thickness increased to 250 μm, ground wall sensor types barely passed 15 kV, and the grayscale count dropped to 120, which is far from PIV image standard.

Cross-Sensor Fingerprint Enhancement Using Adversarial Learning and Edge Loss.

A fingerprint sensor interoperability problem, or a cross-sensor matching problem, occurs when one type of sensor is used for enrolment and a different type for matching. Fingerprints captured for the same person using various sensor technologies have various types of noises and artifacts. This problem motivated us to develop an algorithm that can enhance fingerprints captured using different types of sensors and touch technologies. Inspired by the success of deep learning in various computer vision tasks, we formulate this problem as an image-to-image transformation designed using a deep encoder–decoder model. It is trained using two learning frameworks, i.e., conventional learning and adversarial learning based on a conditional Generative Adversarial Network (cGAN) framework. Since different types of edges form the ridge patterns in fingerprints, we employed edge loss to train the model for effective fingerprint enhancement. The designed method was evaluated on fingerprints from two benchmark cross-sensor fingerprint datasets, i.e., MOLF and FingerPass. To assess the quality of enhanced fingerprints, we employed two standard metrics commonly used: NBIS Fingerprint Image Quality (NFIQ) and Structural Similarity Index Metric (SSIM). In addition, we proposed a metric named Fingerprint Quality Enhancement Index (FQEI) for comprehensive evaluation of fingerprint enhancement algorithms. Effective fingerprint quality enhancement results were achieved regardless of the sensor type used, where this issue was not investigated in the related literature before. The results indicate that the proposed method outperforms the state-of-the-art methods.

Overview of bimetallic nanomaterials used for visualization of latent fingerprints on various surfaces

This review focuses on the current trends in the use of doped metallic nanomaterials in forensic science for the development and detection of latent fingerprints (LFPs) on various surfaces which provide better fingerprint image quality. The advantages and important results of studies conducted on latent fingerprints detection with various doped metallic nanomaterials are critically discussed. We also glimpse on fluorescent nanoparticles that have succeeded in producing high-quality fingerprint images which lead to the extraction of all three levels of fingerprint features. A few metallic nanomaterials used for latent fingerprints detection did not produce high-quality fingerprint images failing extraction of all three levels of fingerprint features. To overcome this forensic problem more research is needed to improve the latent fingerprint detection abilities of doped metallic nanomaterials.

Contactless automated lifting of latent fingerprints from difficult curved surfaces

We present a fully automated method for contactlessly extracting latent fingerprints and correcting for surface curvature distortions. Our proposed method is rapid, automatic, zero-contact and chemical-free, and is able to obtain high quality fingerprint images from (forensically) difficult curved surfaces that are specular and/or transparent, such as glass lightbulbs and chrome water taps. Its contactless nature preserves the prints for later DNA analysis, and we demonstrate that the automatic surface curvature distortion correction improves fingerprint matching scores, often significantly, across a range of objects commonly found at a crime scene.

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.

Fingerprint image enhancement using fully convolutional deep autoencoders / Destaque de imagens de impressão digital utilizando autoencoders profundos totalmente convolucionais

Image quality for fingerprint samples is critical for the matching process. Novel methods introduce deep learning matching techniques based on convolutions neural networks to enhance degraded fingerprint images. However, due to the nature of the enhanced image problem, these methods tend to rely on processing small image patches to achieve their goal. Such an approach may often yield satisfactory results while having high computational costs due to overlapping in patches. In this paper, we propose a fast and accurate fully convolutional neural network based on an auto-encoder architecture to enhance the quality of fingerprint images. We do not use the patch processing method and instead train a model to enhance the image as a whole. After exhaustive testing, we achieve a model that can quickly perform the desired task, while achieving an average of 97.956% and 83.748% per pixel accuracy on the easiest and hardest dataset respectively. The models were trained on the publicly available Fingerprint Verification Competition datasets. We then highlight the most general model that can best enhance the quality of all datasets.

Improving Image Quality Assessment Based on the Combination of the Power Spectrum of Fingerprint Images and Prewitt Filter

The assessment of fingerprint image quality is critical for most fingerprint applications. It has an impact on the performance and compatibility of fingerprint recognition, authentication, and built-in cryptosystems. This paper developed an improved fingerprint image quality assessment derived from the image power spectrum approach and combined it with the Prewitt filter and an improved weighting method. The conventional image power spectrum approach and our proposed approach were implemented for accuracy and reliability tests using good, faulty, and blurred fingerprint images. The experimental results showed the proposed algorithm accurately identified the sharpness of fingerprint images and improved the average difference in FIQMs to 61% between three different levels of blurred fingerprints compared with that achieved by a conventional algorithm.

Directional filter bank-based fingerprint image quality

Directional filter bank-based fingerprint image quality

Fast Fourier transform for fingerprint enhancement and features extraction with adaptive block size using orientation fields

Extracting minutiae from a digital fingerprint is a crucial step in a fingerprint-based recognition systems. This work deals with poor-quality fingerprint images containing broken ridges. The enhancement stage connects broken ridges and is essential for extracting correct minutiae. We use a FFT variant [8] for this stage, but, to truly benefit from FFT in a block, it is essential to determine a suitable block size, depending on ridges orientation field. We propose to use a quadtree to partition the ridges orientation field into homogeneous blocks. A block is homogeneous when at least seventy percent of its ridges orientations are within ten degrees. Another issue addressed in this article is the choice of a suitable neighborhood window size W for computing orientation field image, depending on the fingerprint image quality. The performance improvements of our algorithm are evaluated and compared with standard measures MSE, PSNR and GI, on databases DB1 to DB4 of FVC2004 and NIST special database SD302d.

Fingerprint Image Identification Algorithm Based on Angle Direction of Objects

Fingerprint identification is one of the most important biometric techniques utilized for determining the similarity between the matching images. This paper will be presenting a new identification technique depending on the angle direction of the objects contained in images. The performance of identification techniques relies on the quality of the image. The quality of fingerprint image may not always be well due to many reasons such as contain noise which hinders the clarity of image structures.

P‐76: Adaptive Phase‐Correction Scheme for Enhancing the Sensing Image Quality of an Ultrasonic Fingerprint‐Sensor Integrated OLED System

In this work, we proposed the modified phase correction approach to enhance the fingerprint image quality of the ultrasonic fingerprint sensor on display (FoD). The mechanical wave is diffracted at the boundary between the finger and display panel so that the interference pressure is captured by the sensor. Diffracted waves play a critical role in enhancing sensing images. To correct the diffracted patterns, the corresponding point spread function (PSF) based convolution has been generally required in the conventional approach. In this study, we proposed a modified phase correction method using the sinusoidal implicit typed function, which is derived from the desired magnitude and phase spectrum based on the analyzed pressure distribution. The proposed approach is available for the multi‐physical structure such as acoustic, elastic and viscoelastic mediums in the multi‐layered structure. We verified the proposed approach to be a useful process for diffraction correction in comparison with the numerical and experimental results.

A method for measuring the quality of friction skin impression evidence: Method development and validation

The forensic fingerprint community has faced increasing criticism by scientific and legal commentators, challenging the validity and reliability of fingerprint evidence due to the lack of an empirical basis to assess the quality of the friction ridge impressions. This paper presents a method, developed as a stand-alone software application, DFIQI (“Defense Fingerprint Image Quality Index”), which measures the clarity of friction ridge features (locally) and evaluates the quality of impressions (globally) across three different scales: value, complexity, and difficulty. Performance was evaluated using a variety of datasets, including datasets designed to simulate casework and a dataset derived directly from casework under operational conditions. The results show performance characteristics that are consistent with experts’ subjective determinations. This method provides fingerprint experts: (1) a more rigorous approach by providing an empirical foundation to support their subjective determinations from the Analysis phase of the examination methodology, (2) a framework for organizations to establish transparent, measurable, and demonstrable criteria for Value determinations, (3) and a means of flagging impressions that are vulnerable to erroneous outcomes or inconsistency between experts (e.g., higher complexity and difficulty), and (4) a method for quantitatively summarizing the overall quality of impressions for ensuring representative distributions for samples used in research designs, proficiency testing and error rate testing, and other applications by forensic science stakeholders.