Feature extraction and learning approaches for cancellable biometrics:A survey

Biometric recognition is a widely used technology for user authentication.In the application of this technology,biometric security and recognition accuracy are two important issues that should be considered.In terms of biometric security,cancellable biometrics is an effective technique for protecting biometric data.Regarding recognition accuracy,feature representation plays a significant role in the performance and reliability of cancellable biometric systems.How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community,especially from researchers of cancellable biometrics.Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance,while the privacy of biometric data is protected.This survey informs the progress,trend and challenges of feature extraction and learning for cancellable biometrics,thus shedding light on the latest developments and future research of this area.

A Comprehensive Survey for Privacy-Preserving Biometrics: Recent Approaches, Challenges, and Future Directions

The rapid growth of smart technologies and services has intensified the challenges surrounding identity authenti-cation techniques.Biometric credentials are increasingly being used for verification due to their advantages over traditional methods,making it crucial to safeguard the privacy of people’s biometric data in various scenarios.This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems.It proposes a noble and thorough taxonomy survey for privacy-preserving techniques,as well as a systematic framework for categorizing the field’s existing literature.We review the state-of-the-art methods and address their advantages and limitations in the context of various biometric modalities,such as face,fingerprint,and eye detection.The survey encompasses various categories of privacy-preserving mechanisms and examines the trade-offs between security,privacy,and recognition performance,as well as the issues and future research directions.It aims to provide researchers,professionals,and decision-makers with a thorough understanding of the existing privacy-preserving solutions in biometric recognition systems and serves as the foundation of the development of more secure and privacy-preserving biometric technologies.

Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

The demand for a non-contact biometric approach for candidate identification has grown over the past ten years.Based on the most important biometric application,human gait analysis is a significant research topic in computer vision.Researchers have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far away.Gait recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access control.These systems require a complex combination of technical,operational,and definitional considerations.The employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked applications.Thiswork proposes a novel deep learning-based framework for human gait classification in video sequences.This framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing clothes.The proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer learning.Next,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)algorithm.Then,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization algorithm.This algorithm chooses the best features,combined in a novel correlation-based fusion technique.Finally,the fused best features are categorized using medium,bi-layer,and tri-layered neural networks.On the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was achieved.The achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this work.

Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy issues.Therefore,biometric systems have emerged as a technology with the capability to identify or authenticate individuals based on their physiological and behavioral characteristics.Among different viable biometric modalities,the human ear structure can offer unique and valuable discriminative characteristics for human recognition systems.In recent years,most existing traditional ear recognition systems have been designed based on computer vision models and have achieved successful results.Nevertheless,such traditional models can be sensitive to several unconstrained environmental factors.As such,some traits may be difficult to extract automatically but can still be semantically perceived as soft biometrics.This research proposes a new group of semantic features to be used as soft ear biometrics,mainly inspired by conventional descriptive traits used naturally by humans when identifying or describing each other.Hence,the research study is focused on the fusion of the soft ear biometric traits with traditional(hard)ear biometric features to investigate their validity and efficacy in augmenting human identification performance.The proposed framework has two subsystems:first,a computer vision-based subsystem,extracting traditional(hard)ear biometric traits using principal component analysis(PCA)and local binary patterns(LBP),and second,a crowdsourcing-based subsystem,deriving semantic(soft)ear biometric traits.Several feature-level fusion experiments were conducted using the AMI database to evaluate the proposed algorithm’s performance.The obtained results for both identification and verification showed that the proposed soft ear biometric information significantly improved the recognition performance of traditional ear biometrics,reaching up to 12%for LBP and 5%for PCA descriptors;when fusing all three capacities PCA,LBP,and soft traits using k-nearest neighbors(KNN)classifier.

Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade,owing to the continuing advances in image processing and computer vision approaches.In multiple real-life applications,for example,social media,content-based face picture retrieval is a well-invested technique for large-scale databases,where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures.Humans widely employ faces for recognizing and identifying people.Thus,face recognition through formal or personal pictures is increasingly used in various real-life applications,such as helping crime investigators retrieve matching images from face image databases to identify victims and criminals.However,such face image retrieval becomes more challenging in large-scale databases,where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face images.This work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing(LSH)for facial hard and soft biometrics as(Hard BioHash)and(Soft BioHash),respectively,to be used as a search input for retrieving the top-k matching faces.Moreover,we propose the multi-biometric score-level fusion of both face hard and soft BioHashes(Hard-Soft BioHash Fusion)for further augmented face image retrieval.The experimental outcomes applied on the Labeled Faces in the Wild(LFW)dataset and the related attributes dataset(LFW-attributes),demonstrate that the retrieval performance of the suggested fusion approach(Hard-Soft BioHash Fusion)significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation,where the suggested method provides an augmented accuracy of 87%when executed on 1000 specimens and 77%on 5743 samples.These results remarkably outperform the results of the Hard BioHash method by(50%on the 1000 samples and 30%on the 5743 samples),and the Soft BioHash method by(78%on the 1000 samples and 63%on the 5743 samples).

Estimating Anthropometric Soft Biometrics:An Empirical Method

Following the success of soft biometrics over traditional biomet-rics,anthropometric soft biometrics are emerging as candidate features for recognition or retrieval using an image/video.Anthropometric soft biometrics uses a quantitative mode of annotation which is a relatively better method for annotation than qualitative annotations adopted by traditional biometrics.However,one of the most challenging tasks is to achieve a higher level of accuracy while estimating anthropometric soft biometrics using an image or video.The level of accuracy is usually affected by several contextual factors such as overlapping body components,an angle from the camera,and ambient conditions.Exploring and developing such a collection of anthropometric soft biometrics that are less sensitive to contextual factors and are relatively easy to estimate using an image or video is a potential research domain and it has a lot of value for improved recognition or retrieval.For this purpose,anthro-pometric soft biometrics,which are originally geometric measurements of the human body,can be computed with ease and higher accuracy using landmarks information from the human body.To this end,several key contributions are made in this paper;i)summarizing a range of human body pose estimation tools used to localize dozens of different multi-modality landmarks from the human body,ii)a critical evaluation of the usefulness of anthropometric soft biometrics in recognition or retrieval tasks using state of the art in the field,iii)an investigation on several benchmark human body anthropometric datasets and their usefulness for the evaluation of any anthropometric soft biometric system,and iv)finally,a novel bag of anthropometric soft biomet-rics containing a list of anthropometrics is presented those are practically possible to measure from an image or video.To the best of our knowledge,anthropometric soft biometrics are potential features for improved seamless recognition or retrieval in both constrained and unconstrained scenarios and they also minimize the approximation level of feature value estimation than traditional biometrics.In our opinion,anthropometric soft biometrics constitutes a practical approach for recognition using closed-circuit television(CCTV)or retrieval from the image dataset,while the bag of anthropometric soft biometrics presented contains a potential collection of biometric features which are less sensitive to contextual factors.

A Novel Fusion System Based on Iris and Ear Biometrics for E-exams

With the rapid spread of the coronavirus epidemic all over the world,educational and other institutions are heading towards digitization.In the era of digitization,identifying educational e-platform users using ear and iris based multi-modal biometric systems constitutes an urgent and interesting research topic to pre-serve enterprise security,particularly with wearing a face mask as a precaution against the new coronavirus epidemic.This study proposes a multimodal system based on ear and iris biometrics at the feature fusion level to identify students in electronic examinations(E-exams)during the COVID-19 pandemic.The proposed system comprises four steps.Thefirst step is image preprocessing,which includes enhancing,segmenting,and extracting the regions of interest.The second step is feature extraction,where the Haralick texture and shape methods are used to extract the features of ear images,whereas Tamura texture and color histogram methods are used to extract the features of iris images.The third step is feature fusion,where the extracted features of the ear and iris images are combined into one sequential fused vector.The fourth step is the matching,which is executed using the City Block Dis-tance(CTB)for student identification.Thefindings of the study indicate that the system’s recognition accuracy is 97%,with a 2%False Acceptance Rate(FAR),a 4%False Rejection Rate(FRR),a 94%Correct Recognition Rate(CRR),and a 96%Genuine Acceptance Rate(GAR).In addition,the proposed recognition sys-tem achieved higher accuracy than other related systems.

Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

Due to the enormous usage of the internet for transmission of data over a network,security and authenticity become major risks.Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server.To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical(Face,fingerprint,Ear etc.)and behavioural(Gesture,Voice,tying etc.)by means of matching and verification process.In this work,biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data.The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates.Here Face Attribute Convolutional Neural Network(FACNN)is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing.Implementing Artificial Intelligence(AI)into the proposed system,automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing.Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification.Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity.This system enhances the system performance by 8%matching and verification and minimizes the False Acceptance Rate(FAR),False Rejection Rate(FRR)and Equal Error Rate(EER)by 6 times and increases the data privacy through the biometric cryptosystem by 98.2%while compared to other work.

An Efficient GCD-Based Cancelable Biometric Algorithm for Single and Multiple Biometrics

Cancelable biometrics are required in most remote access applications that need an authentication stage such as the cloud and Internet of Things(IoT)networks.The objective of using cancelable biometrics is to save the original ones from hacking attempts.A generalized algorithm to generate cancelable templates that is applicable on both single and multiple biometrics is proposed in this paper to be considered for cloud and IoT applications.The original biometric is blurred with two co-prime operators.Hence,it can be recovered as the Greatest Common Divisor(GCD)between its two blurred versions.Minimal changes if induced in the biometric image prior to processing with co-prime operators prevents the recovery of the original biometric image through a GCD operation.Hence,the ability to change cancelable templates is guaranteed,since the owner of the biometric can pre-determine and manage the minimal change induced in the biometric image.Furthermore,we test the utility of the proposed algorithm in the single-and multi-biometric scenarios.The multi-biometric scenario depends on compressing face,fingerprint,iris,and palm print images,simultaneously,to generate the cancelable templates.Evaluation metrics such as Equal Error Rate(EER)and Area and Receiver Operator Characteristic curve(AROC)are considered.Simulation results on single-and multi-biometric scenarios show high AROC values up to 99.59%,and low EER values down to 0.04%.

Multimodal Belief Fusion for Face and Ear Biometrics

This paper proposes a multimodal biometric system through Gaussian Mixture Model (GMM) for face and ear biometrics with belief fusion of the estimated scores characterized by Gabor responses and the proposed fusion is accomplished by Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gabor wavelet filters to extracts spatially enhanced Gabor facial features and Gabor ear features. Further, GMM is applied to the high-dimensional Gabor face and Gabor ear responses separately for quantitive measurements. Expectation Maximization (EM) algorithm is used to estimate density parameters in GMM. This produces two sets of feature vectors which are then fused using Dempster-Shafer theory. Experiments are conducted on two multimodal databases, namely, IIT Kanpur database and virtual database. Former contains face and ear images of 400 individuals while later consist of both images of 17 subjects taken from BANCA face database and TUM ear database. It is found that use of Gabor wavelet filters along with GMM and DS theory can provide robust and efficient multimodal fusion strategy.

Preliminary Biometrics of ECG Signal Based on Temporal Organization through the Implementation of a Multilayer Perceptron Neural Network

The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.

Biometrics:Standing Throughout Emerging Technologies

Biometrics technologies have been around for quite some time and many have been deployed for different applications all around the world, ranging from small companies' time and attendance systems to access control systems for nuclear facilities. Biometrics offer a reliable solution for the establishment of the distinctiveness of identity based on 'who an individual is', rather than what he or she knows or carries. Biometric Systems automatically verify a person's identity based on his/her anatomical and behavioral characteristics. Biometric traits represent a strong and undeviating link between a person and his/her identity, these traits cannot be easily lost or forgotten or faked, since biometric systems require the user to be present at the time of authentication. Some biometric systems are more reliable than others, yet they are neither secure nor accurate, all biometrics have their strengths and weaknesses. Although some of these systems have shown reliability and solidarity, work still has to be done to improve the quality of service they provide. Presented is the available standing biometric systems showing their strengths and weaknesses and also emerging technologies which may have great benefits for security applications in the near future.

Diagnosis of inflammatory bowel disease: Potential role of molecular biometrics

Accurate diagnosis of predominantly colonic inflammatory bowel disease(IBD) is not possible in 30% of patients. For decades, scientists have worked to find a solution to improve diagnostic accuracy for IBD, encompassing Crohn's colitis and ulcerative colitis. Evaluating protein patterns in surgical pathology colectomy specimens of colonic mucosal and submucosal compartments, individually, has potential for diagnostic medicine by identifying integrally independent, phenotype-specific cellular and molecular characteristics. Mass spectrometry(MS) and imaging(I) MS are analytical technologies that directly measure molecular species in clinical specimens, contributing to the in-depth understanding of biological molecules. The biometric-system complexity and functional diversity is well suited to proteomic and diagnostic studies. The direct analysis of cells and tissues by Matrix-Assisted-Laser Desorption/Ionization (MALDI) MS/IMS has relevant medical diagnostic potential. MALDI-MS/IMS detection generates molecular signatures obtained from specific cell types within tissue sections. Herein discussed is a perspective on the use of MALDI-MS/IMS and bioinformatics technologies for detection of molecular-biometric patterns and identification of differentiating proteins. I also discuss a perspective on the global challenge of transferring technologies to clinical laboratories dealing with IBD issues. The significance of serologic-immunometric advances is also discussed.

Biometrics of the Cervical Spinal Canal and Cord by Computer Tomography in Togo

Studies on the canal dimensions of the cervical spinal are rare in Africa. The aim of this study is to provide normal values of the cervical spinal canal and spinal cord dimensions of adult people in Togo. It was about a twelve-year prospective study conducted in the main Hospitals of Campus Teaching Hospital. This study involved people of more than 18 years who neither presented any clinical sign nor rachis defect. The distances measured were antero-posterior 1 (APD1) and inter-pedicular (IPD) of the cervical spinal canal, the antero-posterior 2 (APD2) and the transverse (TD) diameters of the cervical spinal cord, followed with APD2/APD1 (R1) and TD/IPD (R2) research reports. The mean age was 38 +/- 9.34 years old. The average of APD1 of the cervical spinal canal stood at 15.41 ± 0.55 mm, with a minimum of 10.48 ± 0.57 mm and a maximum of 25.00 ± 2.60 mm. The IPD average stood at 23.27 ± 1.67 mm with a minimal average of 13.68 ± 1.46 mm and a maximal average of 33.68 ± 1.46 mm. The average of DAP2 was 11.66 ± 0.66 mm, with a minimum of 10.7 ± 0.66 mm and a maximum of 12.77 ± 0.66 mm. The DT average stood at 15.55 ± 1.54 mm, with a minimal average of 14.03 ± 2.43 mm and a maximal average of 17.63 ± 1.82 mm. The ratio R1 (APD2/APD1) average was 0.80 ± 0.04, with a minimum of 0.76 ± 0.06 and a maximum of 0.85 ± 0.07. The ratio R2 (TD/IPD) average stood at 0.69 ± 0.14, with a minimum of 0.5 ± 0.12 and a maximum of 0.84 ± 0.08. The cervical spinal canal and the cervical spinal cord diameters in Togo are not significantly different from those described above.

Visualization of the Machine Learning Process Using J48 Decision Tree for Biometrics through ECG Signal

The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics can be ascertained by recording the electrical signal activity of the heart through the acquisition of an electrocardiogram (ECG). With the application of machine learning the subject specific ECG signal can be differentiated. However, the process of distinguishing subjects through machine learning may be considered esoteric, especially for contributing subject matter experts external to the domain of machine learning. A resolution to this dilemma is the application of the J48 decision tree available through the Waikato Environment for Knowledge Analysis (WEKA). The J48 decision tree elucidates the machine learning process through a visualized decision tree that attains classification accuracy through the application of thresholds applied to the numeric attributes of the feature set. Additionally, the numeric attributes of the feature set for the application of the J48 decision tree are derived from the temporal organization of the ECG signal maxima and minima for the respective P, Q, R, S, and T waves. The J48 decision tree achieves considerable classification accuracy for the distinction of subjects based on their ECG signal, for which the machine learning model is briskly composed.

Intra-Palm Propagation Signals as Suitable Biometrics for Successive Authentication

We propose the use of intra-palm propagation signals as biometrics. The intra-palm propagation signal is a signal that is propagated in the shallow part of the skin of a palm. In this paper, we prepare dedicated measuring devices and measure intra-palm propagation signals from twenty-one experimental subjects. We also evaluate the verification performance based on Euclidian distance or SVM （support vector machine）. The equal error rate in the case of SVM is aovroximatelv 24%.

Key Incorporation Scheme for Cancelable Biometrics

Biometrics is becoming an important method for human identification. However, once a biometric pattern is stolen, the user will quickly run out of alternatives and all the applications where the associated biometric pattern is used become insecure. Cancelable biometrics is a solution. However, traditional cancelable biometric methods treat the transformation process and feature extraction process independently. As a result, this kind of cancelable biometric approach would reduce the recognition accuracy. In this paper, we first analyzed the limitations of traditional cancelable biometric methods, and proposed the Key Incorporation Scheme for Cancelable Biometrics approach that could increase the recognition accuracy while achieving “cancelability”. Then we designed the Gabor Descriptor based Cancelable Iris Recognition method that is a practical implementation of the proposed Key Incorporation Scheme. The experimental results demonstrate that our proposed method can significantly improve the iris recognition accuracy while achieving “cancelability”.

Biometrics as a Matrix: The Short Distance between Crime and Security Systems, Prompting an Artificial Intelligence to Invent Electronic Biometrics ID!

This paper reviews the essential biometrics and develops a way to combine them with the Computer and User Information, giving us an Electronic Biometrics ID. This way, distributed databases contain imperative data from much helpful information that supports more security. We reviewed examples of what these databases would look like, which any responsible party could design to be global. As will be mentioned later, we obtain common international databases whose data are modified according to factors such as the owner of the device, the location of the device, and so on. This is very useful for tracking, and it combines biometrics with data set to give us a comprehensive electronic identification.

A New Useful Biometrics Tool Based on 3D Brain Human Geometrical Characterizations

Previous clinical research studies consider the existence of differences among normal individual cerebral cortex and show that some features such as cerebral sulci are unique for each individual so that human brains are anatomically different and unique to each individual. In this work, we highlight the idea which consists in using medical data, such as brain MRI images for the purpose of individual identification or verification. In other words, we raise the following question: can one identify individuals using their brain geometry characteristics? Our aim is to validate the feasibility of this new biometrics tool based on human brain characterization. The proposed approach differs from existent biometrics modalities (e.g. fingerprint, hand, etc.) in the sense that brain features cannot be modified by individuals as it is the case when using fake fingerprints, or fake hands. In this work, we consider volumetric Magnetic Resonance Images (MRI) from which brain shapes are extracted using a 3D level-sets segmentation approach. Afterwards, geometrical descriptors are extracted from the 3D brain volume and from a projected version which provide specific features such as the isoperimetric ratio, the cortical surface curvature and the Gyrification index. Evaluations performed on a set of MRI images obtained from (MeDEISA) database show that it is possible to distinguish between individuals using their brain characteristics. Preliminary results are particularly encouraging.

Artificial Intelligence-Based Semantic Segmentation of Ocular Regions for Biometrics and Healthcare Applications

Multiple ocular region segmentation plays an important role in different applications such as biometrics,liveness detection,healthcare,and gaze estimation.Typically,segmentation techniques focus on a single region of the eye at a time.Despite the number of obvious advantages,very limited research has focused on multiple regions of the eye.Similarly,accurate segmentation of multiple eye regions is necessary in challenging scenarios involving blur,ghost effects low resolution,off-angles,and unusual glints.Currently,the available segmentation methods cannot address these constraints.In this paper,to address the accurate segmentation of multiple eye regions in unconstrainted scenarios,a lightweight outer residual encoder-decoder network suitable for various sensor images is proposed.The proposed method can determine the true boundaries of the eye regions from inferior-quality images using the high-frequency information flow from the outer residual encoder-decoder deep convolutional neural network(called ORED-Net).Moreover,the proposed ORED-Net model does not improve the performance based on the complexity,number of parameters or network depth.The proposed network is considerably lighter than previous state-of-theart models.Comprehensive experiments were performed,and optimal performance was achieved using SBVPI and UBIRIS.v2 datasets containing images of the eye region.The simulation results obtained using the proposed OREDNet,with the mean intersection over union score(mIoU)of 89.25 and 85.12 on the challenging SBVPI and UBIRIS.v2 datasets,respectively.