Hand Geometry Research Articles

Enhancing biometric system selection: A hybrid AHP-neutrosophic fuzzy TOPSIS approach

A biometric system is essential in improving security and authentication processes across a variety of fields. Due to multiple criteria and alternatives, selecting the most suitable biometric system is a complex decision. We employ a hybrid approach in this study, combining the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Analytic Hierarchical Process (AHP). Biometric technologies are ranked using the TOPSIS method according to the relative weights that AHP determines. By applying the neutrosophic set theory, this approach effectively handles the ambiguity and vagueness inherent in decision-making. Fingerprint, face, Iris, Voice, Hand Veins, Hand geometry and signature are the seven biometric technologies that are incorporated in the framework. Seven essential characteristics are accuracy, security, acceptability, speed and efficiency, ease of collection, universality, distinctiveness used to evaluate these technologies. The model seeks to determine which biometric technology is best suited for a particular application or situation by taking these factors into account. This technique may be applied in other domains in the future.

ENHANCE DOCUMENT VALIDATION UIPATH POWERED SIGNATURE VERIFICATION

Abstract—Signatures are widely used as a means of personal identification and verification. Many documents like bank cheques and legal transactions require signature verification. Signature-based verification of a large number of documents is a very difficult and time-consuming task. Consequently, an explosive growth has been observed in biometric personal verification and authentication systems that are connected with quantifiable physical unique characteristics (finger prints, hand geometry, face, ear, iris scan, or DNA) or behavioural features (gait, voice etc.). As traditional identity verification methods such as tokens, passwords, pins etc suffer from some fatal flaws and are incapable to satisfy the security necessities, the paper aims to consider a more reliable biometric feature, signature verification for the considering. We present a survey of signature verification systems. We classify and give an account of the various approaches that have been proposed for signature verification.

A Dataset for Kinship Estimation from Image of Hand Using Machine Learning

Kinship (Familial relationships) detection is crucial in many fields and has applications in biometric security, adoption, forensic investigations, and more. It is also essential during wars and natural disasters like earthquakes since it may aid in reunion, missing person searches, establishing emergency contacts, and providing psychological support. The most common method of determining kinship is DNA analysis which is highly accurate. Another approach, which is noninvasive, uses facial photos with computer vision and machine learning algorithms for kinship estimation. Each part of the Human -body has its own embedded information that can be extracted and adopted for identification, verification, or classification of that person. Kinship recognition is based on finding traits that are shared by every family. We investigate the use of hand geometry for kinship detection, which is a new approach. Because of the available hand image Datasets do not contain kinship ground truth; therefore, we created our own dataset. This paper describes the tools, methodology, and details of the collected MKH, which stands for the Mosul Kinship Hand, images dataset. The images of MKH dataset were collected using a mobile phone camera with a suitable setup and consisted of 648 images for 81 individuals from 14 families (8 hand situations per person). This paper also presents the use of this dataset in kinship prediction using machine learning. Google MdiaPipe was used for hand detection, segmentation, and geometrical key points finding. Handcraft feature extraction was used to extract 43 distinctive geometrical features from each image. A neural network classifier was designed and trained to predict kinship, yielding about 93% prediction accuracy. The results of this novel approach demonstrated that the hand possesses biometric characteristics that may be used to establish kinship, and that the suggested method is a promising way as a kinship indicator.

Improvement Model for Speaker Recognition using MFCC-CNN and Online Triplet Mining

Various biometric security systems, such as face recognition, fingerprint, voice, hand geometry, and iris, have been developed. Apart from being a communication medium, the human voice is also a form of biometrics that can be used for identification. Voice has unique characteristics that can be used as a differentiator between one person and another. A sound speaker recognition system must be able to pick up the features that characterize a person's voice. This study aims to develop a human speaker recognition system using the Convolutional Neural Network (CNN) method. This research proposes improvements in the fine-tuning layer in CNN architecture to improve the Accuracy. The recognition system combines the CNN method with Mel Frequency Cepstral Coefficients (MFCC) to perform feature extraction on raw audio and K Nearest Neighbor (KNN) to classify the embedding output. In general, this system extracts voice data features using MFCC. The process is continued with feature extraction using CNN with triplet loss to obtain the 128-dimensional embedding output. The classification of the CNN embedding output uses the KNN method. This research was conducted on 50 speakers from the TIMIT dataset, which contained eight utterances for each speaker and 60 speakers from live recording using a smartphone. The accuracy of this speaker recognition system achieves high-performance accuracy. Further research can be developed by combining different biometrics objects, commonly known as multimodal, to improve recognition accuracy further.

Kinship Detection Based on Hand Geometry Using ResNet50 Model for Feature Extraction

Kinship Detection Based on Hand Geometry Using ResNet50 Model for Feature Extraction

Kinship Detection Based on Hand Geometry Using ResNet50 Model for Feature Extraction

Kinship Detection Based on Hand Geometry Using ResNet50 Model for Feature Extraction

Improving Accuracy and Security of Identification by Multimodal Biometric Fusion: An Extensive Analysis

In the modern world, trustworthy and secure identity is essential for a number of uses, such as unlocking personal gadgets and gaining access to secure areas. Conventional techniques, such as PINs and passwords, have shown to be vulnerable to fraud and illegal access. These vulnerabilities have been addressed by the development of biometric authentication, which makes use of an individual's distinct physiological and behavioral characteristics. Unimodal biometric systems concentrate on a single feature, although they frequently face difficulties like noise and spoofing assaults. By merging characteristics from several modalities, multimodal biometric systems provide an answer and improve identification robustness and accuracy. In order to increase identification security and accuracy, this article investigates the combination of hand geometry, palm print, fingerprint, and facial biometrics. Different integration situations and modes of operation for multimodal biometric systems are examined, as well as various fusion methodologies. In summary, the paper ends with implications for biometric systems practitioners, academics, and policymakers as well as a discussion of relevant research topics.

Optimized Hand Geometry-Based Biometric Recognition System

In an era characterized by digital interactions and security needs, biometric systems, especially hand geometry-based recognition, offer an advantageous solution. Biometric identification through hand geometry is ideal for low-security applications due to its non-invasive nature and user-friendly features. This research discusses personal identification leveraging hand geometry features, notably without the use of pegs. Such features encompass finger length and width, palm dimensions, deviations, and angles. Image capturing was conducted without pegs. The study contrasts the use of 12 versus 21 hand geometry features. Identification was achieved using the Euclidean distance measure. The outcomes were validated on both a local and a standard database.

Security system based on hand geometry and palmprint for user authentication in E-correction system

Security system based on hand geometry and palmprint for user authentication in E-correction system

Consistent 3D Hand Reconstruction in Video via Self-Supervised Learning.

We present a method for reconstructing accurate and consistent 3D hands from a monocular video. We observe that the detected 2D hand keypoints and the image texture provide important cues about the geometry and texture of the 3D hand, which can reduce or even eliminate the requirement on 3D hand annotation. Accordingly, in this work, we propose S2HAND, a self-supervised 3D hand reconstruction model, that can jointly estimate pose, shape, texture, and the camera viewpoint from a single RGB input through the supervision of easily accessible 2D detected keypoints. We leverage the continuous hand motion information contained in the unlabeled video data and explore S2HAND(V), which uses a set of weights shared S2HAND to process each frame and exploits additional motion, texture, and shape consistency constrains to obtain more accurate hand poses, and more consistent shapes and textures. Experiments on benchmark datasets demonstrate that our self-supervised method produces comparable hand reconstruction performance compared with the recent full-supervised methods in single-frame as input setup, and notably improves the reconstruction accuracy and consistency when using the video training data.

Deep Learning Strategies for Netbanking Fraud Detection using Face Biometrics

The goal of Internet banking is to develop a secure authentication system for the banking sector. Security providers for online banking must be responsive to security requirements. While there is no doubt that banking transactions should be protected from threats or security attacks on multiple levels, service providers should incorporate security into their service offerings. And you have probably heard a lot about hacker methods for stealing any logical password or pin number, identity theft, credit card fraud, or other security breaches. Identification, which can be equated to a username in the existing framework, is used to authorise and grant access to a system. Because usernames are easily forgotten, they are used to verify that the intended user is who he or she claims to be - the authentication process. Biometric authentication and identity systems are the most recent solutions to security and privacy concerns. Face Recognition is the study of physical or behavioural characteristics of humans that are used to identify people and verify whether they are authorised or not. These physical characteristics of a person include fingerprints, face, hand geometry, voice, palm -vein, and iris biometric device. Create a real-time authentication system for online banking that uses face biometrics for approved users. The project's goal is to create a fully functional face recognition and verification system, as well as to understand the key features of these important technologies, specifically those relating to the technological, application domain, social environmental system, and performance factors. And provide a joint account access system to allow multiple people to access the same accounts by giving original account holders access control. The blockchain is created when a transaction is stored in a database. When a secondary user logs into the account, the access information is sent directly to the primary account holder. This blockchain technology will secure the authentication system of the net banking application.

Reliable Multi-Factor User Authentication With One Single Finger Swipe

Multi-factor user authentication becomes increasingly popular due to its superior security comparing with single-factor user authentication. However, existing multi-factor user authentication methods usually require multiple interactions between users and different authentication components when inputting the multiple factors, leading to extra overhead and bad user experience. In this paper, we propose a secure and user-friendly multi-factor user authentication system named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BioDraw</i> . It utilizes four categories of biometrics (impedance, geometry, behavior, and composition) of human hand plus the pattern-based password to identify and authenticate users. User only needs to draw a pattern on a radio frequency identification tag array, while four biometrics can be collected simultaneously. Specifically, we first design a gradient-based pattern recognition algorithm to precisely extract user’s secret pattern. Then, a convolutional neural network-and long short-term memory-based classifier is utilized for user recognition. Furthermore, to guarantee the systemic security, an anti-replay method called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Binary ALOHA</i> is proposed to detect replayed signals. We conduct extensive experiments with 30 volunteers. The experiment results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BioDraw</i> can achieve high authentication accuracy (with a 2% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> false reject rate) and is effective in defending against various attacks.

Recognition Performance Analysis of a Multimodal Biometric System Based on the Fusion of 3D Ultrasound Hand-Geometry and Palmprint

Multimodal biometric systems are often used in a wide variety of applications where high security is required. Such systems show several merits in terms of universality and recognition rate compared to unimodal systems. Among several acquisition technologies, ultrasound bears great potential in high secure access applications because it allows the acquisition of 3D information about the human body and is able to verify liveness of the sample. In this work, recognition performances of a multimodal system obtained by fusing palmprint and hand-geometry 3D features, which are extracted from the same collected volumetric image, are extensively evaluated. Several fusion techniques based on the weighted score sum rule and on a wide variety of possible combinations of palmprint and hand geometry scores are experimented with. Recognition performances of the various methods are evaluated and compared through verification and identification experiments carried out on a homemade database employed in previous works. Verification results demonstrated that the fusion, in most cases, produces a noticeable improvement compared to unimodal systems: an EER value of 0.06% is achieved in at least five cases against values of 1.18% and 0.63% obtained in the best case for unimodal palmprint and hand geometry, respectively. The analysis also revealed that the best fusion results do not include any combination between the best scores of unimodal characteristics. Identification experiments, carried out for the methods that provided the best verification results, consistently demonstrated an identification rate of 100%, against 98% and 91% obtained in the best case for unimodal palmprint and hand geometry, respectively.

Effect of bamboo hand sheet types on geometry and mechanical properties of the bamboo hand sheet/Green Epoxy composites fabricated by vacuum-injection technique

In order to fabricate bamboo hand sheet/Green Epoxy composites (BHGECs), the individual bamboo fibers are extracted by using chemical solutions. The bamboo fibers are then used to make bamboo hand sheet before being combined with Green Epoxy. The compatibility of fiber and matrix is directly affected by the extracted bamboo fiber shape. The objective of this research is to investigate the suitable chemical solution for extracting and forming bamboo fiber as bamboo hand sheet for reinforcing BHGECs. The bamboo strips were cooked with the different chemical solutions (acid 85%, NaOH 1%, NaOH 6%, and NaOH 30%) in the reactor at 120° C for 2 h. After washing and drying, the cooked bamboo fibers were then formed as the bamboo hand sheet by L’HOMARGY machine. These bamboo hand sheets were then combined with the Infu Green810 (Green Epoxy) by vacuum-injection technique which was developed in this research. The resulting composite materials were tested to derive tensile and bending strength according to ASTM D638 and ASTM D790 respectively. The best mechanical properties are obtained for 6% NaOH-BHGECs since the 6% NaOH bamboo hand sheets correspond to the most homogeneous, the straightest, and the longest bamboo fiber compared to other types of bamboo hand sheets.

Identity Verification using Geometry of Human Hands

Biometrics which can be used for identification of individuals based on their physical or behavioural characteristics has gained importance in today’s society where information security is essential. Hand geometry based biometrics systems are gaining acceptance in low to medium security applications. Hand geometry based identification systems utilize the geometric features of the hand like length and width of the fingers, diameter of the palm and the perimeter. The proposed system is a verification system which utilizes these hand geometry features for user authentication. This project introduces an inexpensive, powerful and easy to use hand geometry based biometric person authentication system. One of the novelties of this work comprises on the introduction of hand geometry’s related, position independent, feature extraction and identification which can be useful in problems related to image processing and pattern recognition. Today students’ (class) attendance became more important part for any organizations/institutions. The conventional method of taking attendance by calling names or signing on paper is very time consuming and insecure, hence inefficient. This paper presents the manual students’ attendance management into computerized system for convenience or data reliability. So, the system is developed by the integration of ubiquitous computing systems into classroom for managing the students’ attendance using palm print scanner. The system is designed to implement an attendance management system based on palm print scanner which students need to use their palm to success the attendance where only authentic student can be recorded the attendance during the class. This system takes attendance electronically with the help of the webcam, and the records the attendance in a database. Students’ roll call percentages and their details are easily seen via Graphical User Interface (GUI).

A hybrid learning scheme towards authenticating hand-geometry using multi-modal features

&lt;span lang="EN-US"&gt;Usage of hand geometry towards biometric-based authentication mechanism has been commercially practiced since last decade. However, there is a rising security problem being surfaced owing to the fluctuating features of hand-geometry during authentication mechanism. Review of existing research techniques exhibits the usage of singular features of hand-geometric along with sophisticated learning schemes where accuracy is accomplished at the higher cost of computational effort. Hence, the proposed study introduces a simplified analytical method which considers multi-modal features extracted from hand geometry which could further improve upon robust recognition system. For this purpose, the system considers implementing hybrid learning scheme using convolution neural network and Siamese algorithm where the former is used for feature extraction and latter is used for recognition of person on the basis of authenticated hand geometry. The main results show that proposed scheme offers 12.2% of improvement in accuracy compared to existing models exhibiting that with simpler amendment by inclusion of multi-modalities, accuracy can be significantly improve without computational burden.&lt;/span&gt;

An Empirical Investigation of Human Identity Verification Methods

A recognition technique is essential in practically every industry in the current digital era. It has several advantages and may be used for security, identification, and authentication. The relevance of access control systems based on biometrics has grown in recent years since they have the ability to address the majority of the shortcomings of existing security systems. Automated biometric systems for human identification take a measurement of the body's "signature," compare it to a database, and make an application-specific determination. These biometric methods for personal verification and identification are based on physiological or behavioral traits that are usually recognizable, despite changing over time, such as fingerprints, hand geometry, the face, voice, lip movement, gait, and iris patterns. The purpose of this study is to conduct a thorough literature review in order to pinpoint the most well-known recognition techniques, applications, and obstacles.

Concerning the need to implement the system of collection and use of biometric data of persons who represent the national security of Ukraine

The article examines new ways to search for and recognize persons who pose a threat to the national security of Ukraine, through the implementation of a system for collecting and using biometric data of such persons. It is noted that currently authorized state bodies report to the State Border Guard Service only such information about a person of certain operational interest that allows him to be identified only by a passport or other document certifying his identity. However, such control makes it impossible to identify a person in the event that he provides forged documents or documents that contain inaccurate information about him. This requires the introduction at the state level of a system of collecting and using biometric data of persons who pose a threat to the national security of Ukraine. Attention is drawn to the fact that law enforcement agencies of foreign countries have recently been actively using information and search systems for biometric identification of a person. Biometric identification is a means of identity verification; belongingness of the passport to its owner by recognizing and comparing biometric data (eye color, retina pattern, fingerprints, hand geometry, facial features, etc.) recorded by the carriers of these data with the owner’s personal data. The article provides a list of physiological and behavioral characteristics of a person, on the basis of which systems of biometric authentication are built. It is concluded that in order to ensure the procedure of identification of persons at checkpoints across the state border of Ukraine, it is necessary to introduce mechanisms for the collection and accumulation of information on the biological characteristics of persons who pose a threat to the national security of Ukraine at the legislative level. In particular, it is necessary to determine the list of biological signs that will be collected, to establish the subjects of the security sector responsible for their collection, to introduce a procedure for accumulating this information, to determine the subject responsible for the formation and maintenance of this data bank, as well as to provide this information to interested state institutions and departments.

Integrated Design Fabrication and Control of a Bioinspired Multimaterial Soft Robotic Hand.

Machines that mimic humans have inspired scientists for centuries. Bioinspired soft robotic hands are a good example of such an endeavor, featuring intrinsic material compliance and continuous motion to deal with uncertainty and adapt to unstructured environments. Recent research led to impactful achievements in functional designs, modeling, fabrication, and control of soft robots. Nevertheless, the full realization of life-like movements is still challenging to achieve, often based on trial-and-error considerations from design to fabrication, consuming time and resources. In this study, a soft robotic hand is proposed, composed of soft actuator cores and an exoskeleton, featuring a multimaterial design aided by finite element analysis (FEA) to define the hand geometry and promote finger's bendability. The actuators are fabricated using molding, and the exoskeleton is 3D-printed in a single step. An ON-OFF controller keeps the set fingers' inner pressures related to specific bending angles, even in the presence of leaks. The FEA numerical results were validated by experimental tests, as well as the ability of the hand to grasp objects with different shapes, weights, and sizes. This integrated solution will make soft robotic hands more available to people, at a reduced cost, avoiding the time-consuming design-fabrication trial-and-error processes.

CAFI-AR

Freehand interaction enhances user experience, allowing one to use bare hands to manipulate virtual objects in AR. Yet, it remains challenging to accurately and efficiently detect contacts between real hand and virtual object, due to the imprecise captured/estimated hand geometry. This paper presents CAFI-AR, a new approach for Contact-Aware Freehand Interaction with virtual AR objects, enabling us to automatically detect hand-object contacts in real-time with low latency. Specifically, we formulate a compact deep architecture to efficiently learn to predict hand action and contact moment from sequences of captured RGB images relative to the 3D virtual object. To train the architecture for detecting contacts on AR objects, we build a new dataset with 4,008 frame sequences, each with annotated hand-object interaction information. Further, we integrate CAFI-AR into our prototyping AR system and develop various interactive scenarios, demonstrating fine-grained contact-aware interactions on a rich variety of virtual AR objects, which cannot be achieved by existing AR interaction approaches. Lastly, we also evaluate CAFI-AR, quantitatively and qualitatively, through two user studies to demonstrate its effectiveness in terms of accurately detecting the hand-object contacts and promoting fluid freehand interactions