Hand Biometrics Research Articles

A multi-factor knuckle and nail bed verification tool for forensic imagery analysis

BackgroundThe grooming process involves sexually explicit images or videos sent by the offender to the minor. Although offenders may try to conceal their identity, these sexts often include hand, knuckle, and nail bed imagery. ObjectiveWe present a novel biometric hand verification tool designed to identify online child sexual exploitation offenders from images or videos based on biometric/forensic features extracted from hand regions. The system can match and authenticate hand component imagery against a constrained custody suite reference of a known subject by employing advanced image processing and machine learning techniques. DataWe conducted experiments on two hand datasets: Purdue University and Hong Kong. In particular, the Purdue dataset collected for this study allowed us to evaluate the system performance on various parameters, with specific emphasis on camera distance and orientation. MethodsTo explore the performance and reliability of the biometric verification models, we considered several parameters, including hand orientation, distance from the camera, single or multiple fingers, architecture of the models, and performance loss functions. ResultsResults showed the best performance for pictures sampled from the same database and with the same image capture conditions. ConclusionThe authors conclude the biometric hand verification tool offers a robust solution that will operationally impact law enforcement by allowing agencies to investigate and identify online child sexual exploitation offenders more effectively. We highlight the strength of the system and the current limitations.

Data-Augmentation-Enabled Continuous User Authentication via Passive Vibration Response

Continuous identity authentication is critical for privacy protection throughout an entire user login session. In this paper, we propose a continuous user authentication mechanism namely, which employs the vibration responses from hand biometrics and is passively activated by natural user-device interaction. Hand vibration responses are embedded in the mechanical vibration of a force-bearing body consisting of one mobile device and one user hand. A built-in accelerometer of the device can capture hand-dependent vibration signals. Considering the concealment of vibration generation and the non-replicability of hand structure, it’s difficult for attackers to counterfeit user identity. Moreover, for ensuring the robustness of authentication performance to tapping behavior interference, we construct a data augmentation module jointly leveraging a signal processing and learning-based pipeline. It can generate enough vibration responses representing hand structure biometrics under various behaviors, thereby making comprehensively understand vibration response variation. We prototype on smartphones, and extensive experiments demonstrate that can achieve satisfactory authentication accuracy.

Deep ContactlessS 3D-Middle Finger Knuckle Recognition

Automation security is one of the main concerns of modern times. A safe and reliable identity verification system is in great demand. A biometric verification system can represent a reliable method of identifying an individual. The knuckle pattern is considered to be one of the emerging hand biometrics because it has the potential to identify individuals. This presented work explores the possibility of using 3D middle finger knuckle for biometric identification. The study provides a new simple, trained from scratch but effective deep convolutional neural network model that designed for 3D figure knuckle print recognition. To the best of the author's knowledge, this is the first successful attempt to develop 3D middle finger knuckle based authentication system using Convolutional Neural Network (CNN). Extensive experiment was carried out using (HKPolyU) the 3D knuckle image database of Hong Kong Polytechnic University. The performance of the proposed CNN model has been evaluated using two session data obtained from different camera lenses. The model designed to be implemented in a real-time system. It can be deployed in a small-scale environment like offices, houses, or personal devices, where the training is very easy. The experimental results were very encouraging and showed the potential of using the 3D middle finger knuckle pattern for biometric applications. The results confirmed that despite various challenges compared with other studies in the same field, the proposed method provides an optimistic solution with an accuracy of 71%.

Mathematical thinking about systems – students modeling a biometrics identity verification system

ABSTRACT The aim of this study is to understand how students’ mathematical thinking is activated and nurtured in solving a modeling problem, where the problem situation involves the design of a system. From a STEM integrated perspective, 9th grade students worked on a modeling task aiming to create an identification system based on hand biometrics. The theoretical framework proposes a conceptualization of the interplay between the mathematical modeling process, from a cognitive perspective, and the engineering design process. Central ideas refer to the cyclical nature of both processes and to the sub-processes involved in them. The empirical data were collected in two design-based research cycles with different 9th grade classes. The data from the groups’ audio and video recording and the students’ productions were analyzed under a directed qualitative content analysis informed by theory. The results showed a global pattern in the students’ thinking in solving a design system problem. The overlapping and interplay between the mathematical modeling and the design process was a prominent characteristic of students’ thinking. The modeling cycle was mirrored by a design cycle, with both running in parallel. System thinking pushed and drove students’ mathematical thinking, from the system requirements to the prototype validation.

Spoofing detection on hand images using quality assessment

Recent research on biometrics focuses on achieving a high success rate of authentication and addressing the concern of various spoofing attacks. Although hand geometry recognition provides adequate security over unauthorized access, it is susceptible to presentation attack. This paper presents an anti-spoofing method toward hand biometrics. A presentation attack detection approach is addressed by assessing the visual quality of genuine and fake hand images. A threshold-based gradient magnitude similarity quality metric is proposed to discriminate between the real and spoofed hand samples. The visual hand images of 255 subjects from the Bogazici University hand database are considered as original samples. Correspondingly, from each genuine sample, we acquire a forged image using a Canon EOS 700D camera. Such fake hand images with natural degradation are considered for electronic screen display based spoofing attack detection. Furthermore, we create another fake hand dataset with artificial degradation by introducing additional Gaussian blur, salt and pepper, and speckle noises to original images. Ten quality metrics are measured from each sample for classification between original and fake hand image. The classification experiments are performed using the k-nearest neighbors, random forest, and support vector machine classifiers, as well as deep convolutional neural networks. The proposed gradient similarity-based quality metric achieves 1.5% average classification er ror using the k-nearest neighbors and random forest classifiers. An average classification error of 2.5% is obtained using the baseline evaluation with the MobileNetV2 deep network for discriminating original and different types of fake hand samples.

A Review on Contactless Hand Verification Systems

Biometric system is gaining popularity increasingly since it provides the most sophisticated technology for authentication, verification, and identification. This technology can identify each individual person on the basis of their biometric information such as their face, hand features, signatures, DNA, or iris pattern and thus can impart a secure and convenient method for authentication purposes. Hand biometrics is one of the most widely used biometric systems. There are two approaches for capturing hand biometrics: contact-based and contactless. In this paper, we present a thorough review of the state of the art contactless hand verification systems. We also present the various modules of the general contactless hand verification system and analyze various hand biometrics features.

Impact of the lossy image compression on the biometric system accuracy: a case study of hand biometrics

Impact of the lossy image compression on the biometric system accuracy: a case study of hand biometrics

Impact of the lossy image compression on the biometric system accuracy: a case study of hand biometrics

Biometric recognition systems are used in several cases, to recognise people using images. Storing of large images require large storage space. To reduce the storage space, compression methods are employed. In this paper, we analyse the effect of lossy image compression on the performance of biometric identification systems. We propose a scheme to evaluate the recognition performance at low bitrates of hand images. The images are compressed using set partitioning in hierarchical trees (SPIHT) encoding. A powerful feature extraction algorithm based on quantising the phase information of the local Fourier transform is used. The nearest neighbour (NN) classifier and the support vector machine (SVM) classifier are employed to classify the feature extraction. The obtained results show at the compression does not significantly affect the performance of recognition operation at low bitrate for unimodal and multimodal systems. Thus, the low bitrate images perform equivalent to uncompressed images in the recognition system.

Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies

Due to the COVID-19 pandemic, automated contactless person identification based on the human hand has become very vital and an appealing biometric trait. Since, people are expected to cover their faces with masks, and advised avoiding touching surfaces. It is well-known that usually contact-based hand biometrics suffer from issues like deformation due to uneven distribution of pressure or improper placement on sensor, and hygienic concerns. Whereas, to mitigate such problems, contactless imaging is expected to collect the hand biometrics information without any deformation and leading to higher person recognition accuracy; besides maintaining hygienic and pandemic concerns. Towards this aim, in this paper, an effective multi-biometric scheme for person authentication based on contactless fingerprint and palmprint selfies has been proposed. In this study, for simplicity and efficiency, three local descriptors, i.e., local phase quantization (LPQ), local Ternary patterns (LTP), and binarized statistical image features (BSIF), have been employed to extract salient features from contactless fingerprint and palmprint selfies. The score level fusion based multi-biometric system developed in this work combines the matching scores using two different fusion techniques, i.e., transformation based-rules like triangular norms and classifier based-rules like SVM. Experimental results on two publicly available databases (i.e., PolyU contactless to contact-based fingerprint database and IIT-Delhi touchless palmprint dataset) show that the proposed contactless multi-biometric selfie system can easily outperform uni-biometrics.

Mathematical models and meanings by school and university students in a modelling task

This study involves two classes from different educational levels, namely 9th grade and university. Students in both contexts were given a modelling task that required the development of a hand biometrics recognition system, during which they performed experimentation and simulation. As aims of the study, we look for distinctions and commonalities between the models developed in the two classes and seek to know how simulation and experimentation influence students’ production of meaning. The theoretical framework comprises the relationship between the modelling process and the prototyping process and adopts Peirce’s pragmatic perspective on meaning. The research is of a qualitative nature, assuming the characteristics of a case study. The results reveal many commonalities between the modelling in the two contexts. Moreover, experimentation and simulation were relevant elements for the production of meaning by the students, which is endorsed by a pragmatic perspective on meaning

Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance

Image acutance or edge contrast in an image plays a crucial role in hyperspectral hand biometrics, especially in the local feature representation phase. However, the study of acutance in this application has not received a lot of attention. Therefore, in this paper we propose that there is an optimal range of image acutance in hyperspectral hand biometrics. To locate this optimal range, a thresholded pixel-wise acutance value (TPAV) is firstly proposed to assess image acutance. Then, through convolving with Gaussian filters, a hyperspectral hand image was preprocessed to obtain different TPAVs. Afterwards, based on local feature representation, the nearest neighbor method was used for matching. The experiments were conducted on hyperspectral dorsal hand vein (HDHV) and hyperspectral palm vein (HPV) databases containing 53 bands. The results that achieved the best performance were those where image acutance was adjusted to the optimal range. On average, the samples with adjusted acutance compared to the original improved by a recognition rate (RR) of 29.5% and 45.7% for the HDHV and HPV datasets, respectively. Furthermore, our method was validated on the PolyU multispectral palm print database producing similar results to that of the hyperspectral. From this we can conclude that image acutance plays an important role in hyperspectral hand biometrics.

Robust and adaptive ROI extraction for hyperspectral dorsal hand vein images

Hyperspectral dorsal hand vein image analysis for biometrics is a relatively new technology with great potential. Compared to traditional dorsal hand biometrics that use only one spectral band to capture and analyse the veins, hyperspectral imaging allows additional information to be included. Given the difficulties of processing hyperspectral dorsal hand images, including uneven illuminations, a noisy background, translation, and deformation, this study proposes a robust and adaptive region of interest (ROI) extraction algorithm. First, a novel knuckle pinky invariant point is located. Next, based on this invariant point a key line on the dorsal hand representing one side of the squared ROI is found. The remaining three sides of the ROI can then be formed. To evaluate the proposed method, both identification and verification experiments were conducted on a large hyperspectral dorsal hand vein database. The experimental results showed that the proposed method outperformed other dorsal hand ROI extraction algorithms for each hyperspectral band. Having performed well in both experiments, the groundwork has been laid to further analyse the extracted ROI in terms of feature extraction.

A Multimodal Hand Recognition System Based on Finger Inner-Knuckle Print and Finger Geometry

Security applications based on hand biometrics are good mean for personal authentication and access control. This article proposes a novel multimodal biometric system that makes use of hand fingers’ inner knuckle prints and hand’s fingers’ geometry. Further, both major knuckle print and minor knuckle print are used. A statistical approach for finger geometry features selection is also proposed. Biometric fusion is investigated using different strategies. Experimental results obtained on a contactless database using standard metrics show that the proposed system achieves high recognition rate. Thus, the proposed approach achieves effective authentication accuracy and can be used in real conditions.

Gender and age group classification from multiple soft biometrics traits

We compare the classification accuracies of estimating the global human demographic attributes - gender and age group from three gender and age models trained with hand, voice recordings and fingerprint biometric characteristics, respectively. Biometric data was acquired from the same subjects within six months. Training and test sets were extracted from the acquired datasets. We show that classification accuracy can be improved by fusing scores of the predictions from the three gender models as well as the three age models at the score level using the sum rule. The models were evaluated with disjointed test sets. The results of predicting gender from the three biometric characteristics show a ranking of classification performance in this order: hand, voice and fingerprint. We also observe that fusing the classifier models improves and consolidates classification accuracy. Finally, we propose three new datasets of hand, voice and fingerprint biometrics, different from existing datasets.

Flooding‐based segmentation for contactless hand biometrics oriented to mobile devices

Segmentation is a crucial stage in hand biometric recognition due to its direct influence on the feature extraction process. The actual trend toward contactless biometrics adds new challenges to traditional defiances, which are mainly related to the capturing conditions and the limitations on computational resources. Traditional methods do not succeed when variable capturing conditions are imposed and methods which are able to deal with daily-life situations are, in general, computationally expensive. In this study, a competitive flooding-based segmentation method oriented to mobile devices is proposed in order to achieve a compromised solution between accuracy and computational resources consumption. The method has been evaluated using images coming from five different databases which cover a wide spectrum of capturing conditions, one of them recorded as a part of this study. The results have been compared with other two well known segmentation techniques in terms of both accuracy and computation time.

Knuckle Print Biometrics and Fusion Schemes -- Overview, Challenges, and Solutions

Numerous behavioral or physiological biometric traits, including iris, signature, hand geometry, speech, palm print, face, etc. have been used to discriminate individuals in a number of security applications over the last 30 years. Among these, hand-based biometric systems have come to the attention of researchers worldwide who utilize them for low- to medium-security applications such as financial transactions, access control, law enforcement, border control, computer security, time and attendance systems, dormitory meal plan access, etc. Several approaches for biometric recognition have been summarized in the literature. The survey in this article focuses on the interface between various hand modalities, summary of inner- and dorsal-knuckle print recognition, and fusion techniques. First, an overview of various feature extraction and classification approaches for knuckle print, a new entrant in the hand biometrics family with a higher user acceptance and invariance to emotions, is presented. Next, knuckle print fusion schemes with possible integration scenarios, and traditional capturing devices have been discussed. The economic relevance of various biometric traits, including knuckle print for commercial and forensic applications is debated. Finally, conclusions related to the scope of knuckle print as a biometric trait are drawn and some recommendations for the development of hand-based multimodal biometrics have been presented.

Recovering and matching minutiae patterns from finger knuckle images

Personal identification approaches using finger knuckle patterns are receiving increasing attention in the biometrics literature. Several approaches have been explored and these methods consider knuckle patterns as textured-like patterns, similar to the iris, and illustrate promising results. However much of the law-enforcement and forensic analysis for hand biometrics still relies on recovery and matching of minutiae patterns which has matured in last several decades. This is largely due to the fact that the identification of minutiae patterns is believed to be more scientific and pervasive in connecting with the anatomy/uniqueness of individuals. Availability of high resolution finger dorsal images acquired for recreational or covert imaging can provide important cues for forensic investigation and analysis, especially when finger dorsal patterns are only the piece of evidence available for the identification. Identification of finger knuckle patterns using minutiae recovery and matching is expected to significantly help in prosecution of such suspects. This paper therefore investigates recovery and matching of minutiae patterns using finger knuckle images. We investigate effective use of minutiae quality in improving performance for the knuckle pattern matching. Our study detailed in this paper also presents comparative evaluation of performance using three popular minutiae matching approaches. The experimental results presented in this paper from a database from 120 different subjects are highly encouraging and validate such first attempt to study minutiae recovery and matching from finger knuckle images.

Illumination-invariant and deformation-tolerant inner knuckle print recognition using portable devices.

We propose a novel biometric recognition method that identifies the inner knuckle print (IKP). It is robust enough to confront uncontrolled lighting conditions, pose variations and low imaging quality. Such robustness is crucial for its application on portable devices equipped with consumer-level cameras. We achieve this robustness by two means. First, we propose a novel feature extraction scheme that highlights the salient structure and suppresses incorrect and/or unwanted features. The extracted IKP features retain simple geometry and morphology and reduce the interference of illumination. Second, to counteract the deformation induced by different hand orientations, we propose a novel structure-context descriptor based on local statistics. To our best knowledge, we are the first to simultaneously consider the illumination invariance and deformation tolerance for appearance-based low-resolution hand biometrics. Settings in previous works are more restrictive. They made strong assumptions either about the illumination condition or the restrictive hand orientation. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods in terms of recognition accuracy, especially under uncontrolled lighting conditions and the flexible hand orientation requirement.

New Mobile Phone and Webcam Hand Images Databases for Personal Authentication and Identification

In this work we created two hand image databases, usingmobile phone cameras and webcams. Themajor goal of these databases is to build upon aperson's authentication/identification using hand biometrics,decreasing the need for expensive hand scanners. Both databases consist of 3000 hand images, 3 sessions x 5 images (per person)x 200 persons, and are available to freely download. The test protocol is defined for both databases; simple experiments were conducted using the same protocol. The results were encouraging for most of the persons (accuracy was greater than 80%), except for those who rotated their hands in an exaggerated manner in all directions.

Contact-free palm-vein recognition based on local invariant features.

Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach.