3D Vascular Pattern Extraction from Grayscale Volumetric Ultrasound Images for Biometric Recognition Purposes

Recognition systems based on palm veins are gaining increasing attention as they are highly distinctive and very hard to counterfeit. Most popular systems are based on infrared radiation; they have the merit to be contactless but can provide only 2D patterns. Conversely, 3D patterns can be achieved with Doppler or photoacoustic methods, but these approaches require too long of an acquisition time. In this work, a method for extracting 3D vascular patterns from conventional grayscale volumetric images of the human hand, which can be collected in a short time, is proposed for the first time. It is based on the detection of low-brightness areas in B-mode images. Centroids of these areas in successive B-mode images are then linked through a minimum distance criterion. Preliminary verification and identification results, carried out on a database previously established for extracting 3D palmprint features, demonstrated good recognition performances: EER = 2%, ROC AUC = 99.92%, and an identification rate of 100%. As further merit, 3D vein pattern features can be fused to 3D palmprint features to implement a costless multimodal recognition system

The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Curved Gabor Filters for Fingerprint Image Enhancement

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods

3D Palmprint Recognition Using Dempster-Shafer Fusion Theory

This paper proposed a novel 3D palmprint recognition algorithm by combining 3D palmprint features using D-S fusion theory. Firstly, the structured light imaging is used to acquire the 3D palmprint data. Secondly, two types of unique features, including mean curvature feature and Gaussian curvature feature, are extracted. Thirdly, the belief function of the mean curvature recognition and the Gaussian curvature recognition was assigned, respectively. Fourthly, the fusion belief function from the proposed method was determined by the Dempster-shafer (D-S) fusion theory. Finally, palmprint recognition was accomplished according to the classification criteria. A 3D palmprint database with 1000 range images from 100 individuals was established, on which extensive experiments were performed. The results show that the proposed method 3D palmprint recognition is much more robust to illumination variations and condition changes of palmprint than MCR and GCR. Meanwhile, by fusing mean curvature and Gaussian curvature feature, the experimental results are promising (the average equal error rate of 0.404%). In the future, imaging technique needs further improvement for a better recognition performance

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Contactless Palmprint Recognition System: A Survey

Information systems in organizations traditionally require users to remember their secret pins or (passwords), token, card number, or both to con�rm their identities. However, the technological trend has been moving towards personal identi�cation based on individual behavioural attributes (such as gaits, signature, and voice) or physiological attributes (such as palmprint, �ngerprint, face, iris, or ear). These attributes (biometrics) offer many advantages over knowledge and possession-based approaches. For example, palmprint images have rich, unique features for reliable human identi�cation, and it has received signi�cant attention due to their stability, reliability, uniqueness, and non-intrusiveness. This paper provides an overview and evaluation of contactless palmprint recognition system, the state-of-the-art performance of existing studies, different types of ``Region of Interest'' (ROI) extraction algorithms, feature extraction, and matching algorithms. Finally, the �ndings obtained are presented and discussed

Invariant Scattering Transform for Medical Imaging

Invariant scattering transform introduces new area of research that merges the signal processing with deep learning for computer vision. Nowadays, Deep Learning algorithms are able to solve a variety of problems in medical sector. Medical images are used to detect diseases brain cancer or tumor, Alzheimer's disease, breast cancer, Parkinson's disease and many others. During pandemic back in 2020, machine learning and deep learning has played a critical role to detect COVID-19 which included mutation analysis, prediction, diagnosis and decision making. Medical images like X-ray, MRI known as magnetic resonance imaging, CT scans are used for detecting diseases. There is another method in deep learning for medical imaging which is scattering transform. It builds useful signal representation for image classification. It is a wavelet technique; which is impactful for medical image classification problems. This research article discusses scattering transform as the efficient system for medical image analysis where it's figured by scattering the signal information implemented in a deep convolutional network. A step by step case study is manifested at this research work.Comment: 11 pages, 8 figures and 1 tabl

Patterns Identification of Finger Outer Knuckles by Utilizing Local Directional Number

Finger Outer Knuckle (FOK) is a distinctive biometric that has grown in popularity recently. This results from its inborn qualities such as stability, protection, and specific anatomical patterns. Applications for the identification of FOK patterns include forensic investigations, access control systems, and personal identity. In this study, we suggest a method for identifying FOK patterns using Local Directional Number (LDN) codes produced from gradient-based compass masks. For the FOK pattern matching, the suggested method uses two asymmetric masks—Kirsch and Gaussian derivative—to compute the edge response and extract LDN codes. To calculate edge response on the pattern, an asymmetric compass mask made from the Gaussian derivative mask is created by rotating the Kirsch mask by 45 degrees to provide edge response in eight distinct directions. The edge response of each mask and the combination of dominating vector numbers are examined during the LDN code-generating process. A distance metric can be used to compare the LDN code\u27s condensed representation of the FOK pattern to the original for matching purposes. On the Indian Institute of Technology Delhi Finger Knuckle (IITDFK) database, the efficiency of the suggested procedure is assessed. The data show that the suggested strategy is effective, with an Equal Error Rate (EER) of 10.78%. This value performs better than other EER values when compared to different approaches

The impact of collarette region-based convolutional neural network for iris recognition

Iris recognition is a biometric technique that reliably and quickly recognizes a person by their iris based on unique biological characteristics. Iris has an exceptional structure and it provides very rich feature spaces as freckles, stripes, coronas, zigzag collarette area, etc. It has many features where its growing interest in biometric recognition lies. This paper proposes an improved iris recognition method for person identification based on Convolutional Neural Networks (CNN) with an improved recognition rate based on a contribution on zigzag collarette area - the area surrounding the pupil - recognition. Our work is in the field of biometrics especially iris recognition; the iris recognition rate using the full circle of the zigzag collarette was compared with the detection rate using the lower semicircle of the zigzag collarette. The classification of the collarette is based on the Alex-Net model to learn this feature, the use of the couple (collarette/CNN) allows for noiseless and more targeted characterization and also an automatic extraction of the lower semicircle of the collarette region, finally, the SVM training model is used for classification using grayscale eye image data taken from (CASIA-iris-V4) database. The experimental results show that our contribution proves to be the best accurate, because the CNN can effectively extract the image features with higher classification accuracy and because our new method, which uses the lower semicircle of the collarette region, achieved the highest recognition accuracy compared with the old methods that use the full circle of collarette region

Human Verification using Multiple Fingerprint Texture Matchers

This paper presents a multimodal biometric verification system using multiple fingerprint matchers. Theproposed verification system is based on multiple fingerprint matchers using Spatial Grey LevelDependence Method and Filterbank-based technique. The method independently extract fingerprinttexture features to generate matching scores. These individual normalized scores are combined into afinal score by the sum rule and the final score is eventually used to effect verification of a person asgenuine or an imposter. The matching scores are used in two ways: in first case equal weights are assignedto each matching scores and in second case user specific weights are used. The proposed verificationsystem has been tested on fingerprint database of FVC2002. The experimental results demonstrate that theproposed fusion strategy improves the overall accuracy of the system by reducing the total error rate of thesystem.Keywords: - Multimodal biometric System, Fingerprint verification, SGLDM, Filterbank matching, Scorelevel fusion, Sum rule