Biometric Recognition System Research Articles

Recent advancements in finger vein recognition technology: Methodology, challenges and opportunities

Finger vein recognition biometric trait is a significant biometric modality that is considered more secure, reliable, and emerging. This article presents a review to focus on the recent research landscape in biometric finger vein recognition systems. This article focuses on manuscripts related to keywords ‘Finger Vein Authentication System’, ‘Anti-spoofing or Presentation Attack Detection’, ‘Multimodal Biometric Finger Vein Authentication’ and their variations in four main digital research libraries such as IEEE Xplore, Springer, ACM, and Science Direct. The final set of articles is divided into three main categories: Deep Learning (DL) based finger vein recognition, Presentation Attack Detection (PAD), and Multimodal-based finger vein authentication system. Deep learning-based finger vein recognition techniques are further sub-divided into pre-processing (Quality assessment and enhancement) based, feature extraction based, and feature extraction and recognition based schemes. Presentation attack detection methods are sub-divided into software-based and hardware-based approaches. Multimodal-based finger vein biometric system is sub-categorized into feature level fusion, matching level fusion, and hybrid fusion methods. The authors have studied the problem of the recent algorithm and their solution related to finger vein biometric system from the recent literature. Performance analysis and selected the best promising research work from the mentioned studies are also presented. Finally, open challenges, opportunities, and suggested solutions related to deep learning, PAD, and Multimodal based finger vein recognition systems have been discussed in the discussion section. This work would be helpful to the developers, company users, researchers, and readers to get insight into the importance of such technology and the recent problem faced by finger vein authentication technology with respect to deep learning and multimodal systems.

AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK

Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%.

Forensic Odontological Parameters as Biometric Tool: A Review.

Human identification by virtue of dental identification is on the increase in the past few years and has served as a major adjunct in the identification process. The science associated with the dealing of evidences from dental, oral, and paraoral structures—Forensic Odontology, is a boon in itself. It has been seen that the role of forensic odontology has increased as most of the times teeth and other orally associated structures remain the only means of identification in the worst of scenarios where the conventional biometrics such as fingerprint, iris scan, palm print, etc., may be lost and are therefore not reliable enough to resolve the purpose of identification. There are various important aspects in forensic odontology which are associated with the study of tooth prints, i.e., ameloglyphics, radiographs, the study of palatal rugae patterns, i.e., rugoscopy, cheiloscopy, i.e., the study of lip patterns, tongue prints which can serve as an important biometric tool to aid in the human identification process. Dental biometrics helps to determine several parameters that serve in the interest of forensics such as type of specimen, population affinity, sex, age, stature, race, and various other individualization factors. So, comparative dental identifications with the help of a biometric recognition system will play a major role in identifying deceased individuals in disasters, crimes, or any other mass tragedies.How to cite this article: Kaul B, Vaid V, Gupta S, et al. Forensic Odontological Parameters as Biometric Tool: A Review. Int J Clin Pediatr Dent 2021;14(3):416–419.

Failure of affine‐based reconstruction attack in regenerating vascular feature points

Inverse biometrics methods are a major privacy concern for users of biometric recognition systems. Affine-based reconstruction attack is an inverse biometrics method that models the biometric recognition algorithm by an affine approximation. This type of attack reconstructs targeted biometric references using the modelled biometric recognition algorithm and the comparison scores issued by the system. Although this reconstruction method has only been successfully applied to reconstruct face images, the common consensus is that any biometric system that issues comparison scores could be vulnerable to such an attack since this method is sufficiently general to be applied to other biometric templates. Here it is shown that the attack fails to regenerate sparse vascular feature point templates. The reconstruction attack on feature point patterns extracted from retina and hand vascular images is tested. The inverse attack match rate for reconstructed reference templates was 0.3% in one experiment using retinal vasculature and 0% for all others. These results show that the reconstruction attack is not as catastrophic as it is widely accepted to be, and that vascular biometric template protection schemes that store sparse templates as references and reveal comparison scores are not susceptible to affine-based reconstruction attacks.

DESIGN AND USABILITY OF FACE RECOGNITION BASED ANNOUNCEMENT SYSTEM

The rapid developments in technology have an increasing impact and use on biometric person recognition systems. Facial recognition-based systems, one of the biometric person recognition systems, have been widely used in recent years thanks to their easy implementation, fast integration and simple usage as they do not require any additional equipment. Especially the widespread use of computer vision and cloud-computing based applications, smart face recognition systems have become an indispensable part of our lives in recent years. The use of these systems, which have become widespread in security, health, education, military, shopping mall and industrial areas, has increased more during the pandemic period. Institutions and organizations do not want to allocate time and cost to write their own software for face recognition based systems. The services offered by major cloud computing providers can be used to solve this problem. In this context, the article presents a smart announcement system design using cloud computing based face recognition technology. In the past, making an announcement has been seen as a difficult task. It was thought to be a time consuming task, both because of the cost of printing and because all the operations had to be repeated when there were changes in the announcement. Today, signs have left their places to digital screens. It will especially ensure that announcements, warnings, promotions, and notifications are performed effectively at the developed system for large scale institutions, organizations, factories, universities, shopping malls and health institutions. Facial recognition based smart announcement system detects features such as person recognition, gender, and age estimation at a rate of 100% and displays personal announcements according to their priority status. In addition, according to the experimental studies, it was observed that the person recognition and the presentation of the announcements on the screen took an average of 1.3 seconds. According to the announcement system survey, 85% of those who use the system stated that it is useful and user-friendly.

Towards Protecting and Enhancing Vascular Biometric Recognition Methods via Biohashing and Deep Neural Networks

Biometric template protection has been a crucial concern in biometric recognition systems. This is because biometric characteristics are irreplaceable, so the compromised templates can also be used in other applications. On the other side, deploying template protection algorithms often affects the performance of biometric systems. In this paper, we consider both raw and pre-processed finger vein images and propose a novel deep-learning-based framework to protect biometric templates and enhance recognition performance. We use a deep convolutional auto-encoder structure to reduce the dimension of the feature space, and then secure templates by applying the Biohashing algorithm on the features extracted at the bottleneck layer of our auto-encoder. The experimental results indicate that the protected templates through our framework achieve superior performance than Biohash protected templates of the raw features in the normal scenario. In the stolen scenario, where the Biohashing key is stolen, our model yields far better performance than Biohashing of raw features extracted by previous recognition methods. We also evaluate the generalization of our proposed framework on other vascular biometric modalities. It is worth mentioning that we provide an open-source implementation of our framework so that other researchers can verify our findings and build upon our work.

Detection of 3D face masks with thermal infrared imaging and deep learning techniques

Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural network-featured method for detecting presentation attacks. Full Text: PDF ReferencesS.R. Arashloo, J. Kittler, W. Christmas, "Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features", IEEE Trans. Inf. Forensics Secur. 10, 11 (2015). CrossRef A. Anjos, M.M. Chakka, S. Marcel, "Motion-based counter-measures to photo attacks inface recognition", IET Biometrics 3, 3 (2014). CrossRef M. Killioǧlu, M. Taşkiran, N. Kahraman, "Anti-spoofing in face recognition with liveness detection using pupil tracking", Proc. SAMI IEEE, (2017). CrossRef A. Asaduzzaman, A. Mummidi, M.F. Mridha, F.N. Sibai, "Improving facial recognition accuracy by applying liveness monitoring technique", Proc. ICAEE IEEE, (2015). CrossRef M. Kowalski, "A Study on Presentation Attack Detection in Thermal Infrared", Sensors 20, 14 (2020). CrossRef C. Galdi, et al, "PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT - a case study", IET Biometrics 9, 6 (2020). CrossRef D.A. Socolinsky, A. Selinger, J. Neuheisel, "Face recognition with visible and thermal infrared imagery", Comput. Vis Image Underst. 91, 1-2 (2003) CrossRef L. Sun, W. Huang, M. Wu, "TIR/VIS Correlation for Liveness Detection in Face Recognition", Proc. CAIP, (2011). CrossRef J. Seo, I. Chung, "Face Liveness Detection Using Thermal Face-CNN with External Knowledge", Symmetry 2019, 11, 3 (2019). CrossRef A. George, Z. Mostaani, D Geissenbuhler, et al., "Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network", IEEE Trans. Inf. Forensics Secur. 15, (2020). CrossRef S. Ren, K. He, R. Girshick, J. Sun, "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition", Proc. CVPR IEEE 39, (2016). CrossRef K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition", Proc. CVPR, (2016). CrossRef K. Mierzejewski, M. Mazurek, "A New Framework for Assessing Similarity Measure Impact on Classification Confidence Based on Probabilistic Record Linkage Model", Procedia Manufacturing 44, 245-252 (2020). CrossRef

Fingerprint Authentication

Biometrics refers to the automatic identification of a living person based on physiological or behavioural characteristics for authentication purpose. Among the existing biometric technologies are the face recognisation, fingerprint recognition, finger-geometry, hand geometry, iris recognition, vein recognition, voice recognition and signature recognition, Biometric method requires the physical presence of the person to be identified. This emphasizes its preference over the traditional method of identifying what you have such as, the use of password, a smartcard etc. Also, it potentially prevents unauthorized admittance to access control systems or fraudulent use of ATMs, Time Attendance Systems, cellular phones, smart cards, desktop PCs, Workstations, vehicles and computer networks. Biometric recognition systems offer greater security and convenience than traditional methods of personal recognition.

Application of biometric systems in face identification technologies

The paper presents an analytical review of the application of biometric recognition systems in relation to facial image identification technologies. The classification of biometric systems is presented. The trends of technological progress in the field of biometrics and facial recognition capabilities are considered. It is determined that in 2020 there is a trend of transition from the use of biometric recognition technologies in traditional state security systems to the sphere of commercial and user applications. The process of «linking» encryption keys and passwords with the biometric parameters of the data subject is described. It is proposed that a biometric feature and a biometrics parameter mean a certain value that has a physical meaning that characterizes the subject itself. The possibility of using circular neighborhood and bilinear interpolation of pixel intensity values in biometrics is also presented. This will make it possible to build a local binary template. In order to solve the problem of identification of persons, it is advisable to investigate the essence of biometric systems in the technologies of identification of persons, their types, identifying the shortcomings of each of them, on the basis of which to present the directions of elimination and search for the most reliable technologies. The essence of the use of biometric systems in the technologies of identification of persons is, for example, that the user can provide the bank or other counterparty with evidence that it is he who wants to use the services on his accounts. At the same time, the demand has increased for contactless biometric solutions. These technologies are implemented in order to conduct additional biometric verification of users. This allows to minimize possible fraud or violation of the internal rules of the service, for example, the transfer of accounts of some registered users to others.

A nature‐inspired meta‐heuristic paradigm for person identification using multimodal biometrics

AbstractBecause of the expanding improvement of the internet, security toward individual data has gotten more significant in recent decades. Due to the increasing development of the internet, security toward personal information has become more important in recent decades. Various person recognition methods are introduced for ensuring the security of a person's information. However identifying the information of the user using unique physiological characteristics poses a major issue in the biometric recognition system. Hence, an effective multimodal recognition system is developed using the proposed Elephant Deer Hunting Optimization‐based hybrid fusion (EDHO‐based hybrid fusion) model for person recognition. The proposed EDHO is designed by the integration of Elephant Herding Optimization and Deer Hunting Optimization Algorithm. Here, three different modalities, such as finger vein, dorsal hand vein, and electromyography (EMG) data are employed for the person recognition process. To increase the level of security, the hybrid feature fusion process is employed in such a way that features are fused based on the weight coefficients. The optimization algorithm is considered for the computation of the weight factor. However, the optimal weight value shows the optimal solution of person recognition in such a way that the computation of optimal value is based on the fitness measure. The proposed method achieved higher performance of accuracy as 0.963, True Negative Rate (TNR) as 0.9658, and True Positive rate (TPR) as 0.9548, respectively.

Face Recognition Based on Artificial Neural Network: A Review

The face recognition/detection is considered as one of the most popular applications in the field of image processing and biometric pattern recognition systems. Although the face recognition approach improves authentication procedure, nevertheless still many challenges appear due to diversities in human facial expression, image huge size, background complexity, variation in illumination, poses, blurry, etc. Therefore, the face detection procedure is classified as one of the most difficult tasks in computer vision. This research paper tends to address the concept of image processing along with the use of the Artificial Neural Network approach and represent it is a potential capability in enhancing the method of extracting face pattern through an adaption of various ANN topologies. Furthermore, it represents fundamental phases associated with the construction of any facial recognition system. Finally, it provides a general overview of different literature survives that related to face recognition based on the use of different ANN approaches and algorithms

Cascade‐based Multimodal Biometric Recognition System with Fingerprint and Face

AbstractIn recent years, biometrics has played a vital role in protecting users' privacy and enabling secure authentication. Multimodal security, comprised of identity cards with attached unique passwords, is used to authenticate the genuine or imposter person; however, it is not a perfect security framework. Biometric traits are unique to every individual and hence proved to be very secure. This paper proposes a hybrid approach by combining cascaded and fusion‐based multimodal biometric framework using fingerprint and face traits. The fingerprint and face features are extracted using the minutiae feature extraction algorithm and principal component analysis (PCA) algorithm. The hybrid approach is applied to a self ‐built database of around 450 fingerprints and 450 face images. The performance of the proposed hybrid system is determined using False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), and Accuracy evaluation parameters. The unimodal system of fingerprint and face at Level I and Level II yields an accuracy of 100% at a threshold value of 0.42 and 0.54, respectively. The multimodal system at Level III delivers an accuracy rate of 93.70%, 99.20%, 97.85%, and 99.50% at a threshold value of 0.75, 0.60, 0.30, and 0.60 in sum level fusion, product level fusion, min level fusion, and max level fusion schemes, respectively.

Savitzky-Golay filter energy features-based approach to face recognition using symbolic modeling.

Face recognition is a well-researched domain however many issues for instance expression changes, illumination variations, and presence of occlusion in the face images seriously affect the performance of such systems. A recent survey shows that COVID-19 will also have a considerable and long-term impact on biometric face recognition systems. The work has presented two novel Savitzky–Golay differentiator (SGD) and gradient-based Savitzky–Golay differentiator (GSGD) feature extraction techniques to elevate issues related to face recognition systems. The SGD and GSGD feature descriptors are able to extract discriminative information present in different parts of the face image. In this paper, an efficient and robust person identification using symbolic data modeling approach and similarity analysis measure is devised and employed for feature representation and classification tasks to address the aforementioned issues of face recognition. Extensive experiments and comparisons of the proposed descriptors experimental results indicated that the proposed approaches can achieve optimal performance of 96–97, 92–96, 100, 84–93, and 87–96% on LFW, ORL, AR, IJB-A datasets, and newly devised VISA database, respectively.

User recognition based on periocular biometrics and touch dynamics

• We proposed a multimodal biometric recognition system. • Two sources of behavioural biometric data are analyzed. • Eye movements and touch dynamics improve the performance of recognition systems. • Feature-level fusion approach. • The proposed approach allows to obtain an accuracy of over 92%. Web user behavioural recognition is the process by which web users are identified and distinguished through behavioural features. In this work, two sources of behavioural biometric data are analyzed for the development of this web user identification model, touch dynamics and the characteristics extracted from the periocular area related to the pupils, blinks and fixations. The approach adopted used to improve the overall performance of the multimodal biometric recognition system is based on a fusion at the Feature level to which different distance measure techniques (Euclidean, Bray-Curtis, Manhattan, Canberra, Chebyshev, Cosine) are applied to determine if the test sample belongs to the target subject. To further improve the system performance, we have applied multi-data processing methods such as Canonical Correlation Analysis (CCA) and Principal Component Analysis (PCA). The results obtained demonstrate the promise of these two different biometric traits and, above all, of their fusion. In fact, the fusion approach allows obtaining an accuracy higher than that of individual biometrics, reaching an accuracy of over 92%.

Identification of Baby's Feet Using Principal Component Analysis (PCA) Method Character Extraction with K-Nearest Neighbor (KNN) Classification in Matlab Application

Biometric recognition systems or human identification are very important in security access for identification and verification systems. The biometric recognition system can be used as an identification system based on the characteristics possessed by the body part of each individual. The soles of the feet can be used for identification because the soles of the feet have certain and unique characteristics which include major lines, protrusions, small dots, single points, and textures. The introduction of biometrics in babies is still conventional, which is a standard operating procedure such as attaching bracelets on baby's feet and imprinting or inking on the soles of baby's feet which are affixed to paper and are very vulnerable to the risk of damage or loss of data, there is a need for a system that can store data automatically digital and able to do the baby identification process. The Principal Component Analysis method is used for the extraction process of the characteristics of the baby's feet. The classification uses the K-Nearest Neighbor (K-NN) method with the euclidean distance approach. Tests were carried using 120 images of baby feet, there are 20 classes, each class contains 3 images of the right foot and 3 images of the foot of the left foot, and a dataset of 280 training images. The highest accuracy result obtained in system testing is 91% with a computation time of 5.63 seconds using the Principal Component Analysis method with the K-Nearest Neighbor (K-NN) classification.Keywords: Footprint, Feature Extraction, Principal Component Analysis, K-Nearest Neighbor.

A novel multi-lead ECG personal recognition based on signals functional and structural dependencies using time-frequency representation and evolutionary morphological CNN

Biometric recognition systems have been employed in many aspects of life such as security technologies, data protection, and remote access. Physiological signals, e.g. electrocardiogram (ECG), can potentially be used in biometric recognition. From a medical standpoint, ECG leads have structural and functional dependencies. In fact, precordial ECG leads view the heart from different axial angles, whereas limb leads view it from various coronal angles. This study aimed to design a personal biometric recognition system based on ECG signals by estimating these latent medical variables. To estimate functional dependencies, within-correlation and cross-correlation in time-frequency domain between ECG leads were calculated and represented in the form of extended adjacency matrices. CNN trees were then introduced through genetic programming for the automated estimation of structural dependencies in extended adjacency matrices. CNN trees perform the deep feature learning process by using structural morphology operators. The proposed system was designed for both closed-set identification and verification. It was then tested on two datasets, i.e. PTB and CYBHi, for performance evaluation. Compared with the state-of-the-art methods, the proposed method outperformed all of them.

Review on Data Acquisition of Electrocardiogram Biometric Recognition in Wearable Smart Textile Shirts

Electrocardiogram (ECG) wearable smart textile shirt has widely been investigated due to its high flexibility, reusability, comfort, and the possibility of being used for home-based, real-life activities and real-time measurement. ECG smart textile shirt is an embedded textile sensor inside a cloth that can capture ECG data in more convenient ways and ease user-friendly, especially for continuous and long-term ECG data acquisition outside the laboratory environment. However, the current challenge with ECG smart textile shirt is the reliability and quality of data acquired by the wearable smart textile. This review will mainly focus on the research strategies in the early stages regarding data acquisitions in ECG smart textile shirt. It will introduce researchers’ data acquisition methods in the biometric recognition system using wearable ECG smart textile. The Scopus and Mendeley databases review may help future researchers consider different parameters, which affect the reliability and data quality when selecting data acquisitions strategies in a biometric recognition system using wearable ECG smart textile shirt.

Palmprint features matching based on KAZE feature detection

Palmprint is very popular biometric recognition system that is able to guarantee high accuracy. It has attracted increasing amount of attention because palmprints are abundant of many characteristics, such as the principle lines, ridges, minute points and textures for the use of images with low resolution. In this paper we propose palmprint feature detection based on KAZE technique. Palmprint texture has many important points for discrimination process. Selecting the best number of point using KAZE is very important for classification process in order to avoid overlapping features in different class. The experimental work has been done using polyU palmprint database in order to evaluate the best number of features.

Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

Biometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs), tested on two fingerprint databases—namely, PolyU and NIST—and comparisons to other results presented in the literature in order to establish the type of classification that allows us to obtain the best performance in terms of precision and model efficiency, among approaches under examination, namely: AlexNet, GoogLeNet, and ResNet. We present the first study that extensively compares the most used CNN architectures by classifying the fingerprints into four, five, and eight classes. From the experimental results, the best performance was obtained in the classification of the PolyU database by all the tested CNN architectures due to the higher quality of its samples. To confirm the reliability of our study and the results obtained, a statistical analysis based on the McNemar test was performed.

Perceptual quality-preserving black-box attack against deep learning image classifiers

Deep neural networks provide unprecedented performance in all image classification problems, including biometric recognition systems, key elements in all smart city environments. Recent studies, however, have shown their vulnerability to adversarial attacks, spawning intense research in this field. To improve system security, new countermeasures and stronger attacks are proposed by the day. On the attacker’s side, there is growing interest for the realistic black-box scenario, in which the user has no access to the network parameters. The problem is to design efficient attacks which mislead the neural network without compromising image quality. In this work, we propose to perform the black-box attack along a high-saliency and low-distortion path, so as to improve both attack efficiency and image perceptual quality. Experiments on real-world systems prove the effectiveness of the proposed approach both on benchmark tasks and actual biometric applications.