A Biometric System Design using Finger Knuckle Biological Trait

The authentication, verification, and identification of user can be done using the biometric traits, such as the face, hand vein, finger knuckle, iris, gait, and so on. Among various traits, finger knuckle, face, and hand vein are the important modalities used to recognize the user identity. Due to factors, like image misalignment or illumination variation, the existing unimodal biometric system degrades the performance of recognition. To solve such issues, cryptography enabled multimodal biometric system is developed in this research using the proposed Taylor-Grey Rider based Deep Recurrent Neural Network (Tay-GR based Deep RNN). The proposed Tay-GR is developed by integrating the Taylor series with the Grey Wolf Optimizer (GWO) and Rider Optimization Algorithm (ROA). By integrating the GWO and ROA with Taylor series, the performance of biometric recognition can be increased by reducing the computational cost and time, which can be optimally achieved by updating the Taylor coefficients with the best solution of search agent of wolves. However, the proposed Tay-GR based Deep RNN obtained better performance in terms of accuracy, sensitivity, and specificity with the values of 95.81%, 97.78%, and 95.10%, respectively.

Biometric identification using finger knuckle imaging has generated lot of promises with interesting applications in forensics and remote biometrics. Prior efforts in the biometrics literature have only investigated the `major' finger knuckle patterns that are formed on the finger surface joining proximal phalanx and middle phalanx bones. This paper investigates the possible use of `minor' finger knuckle patterns which are formed on the finger surface joining distal phalanx and middle phalanx bones. The `minor' or `upper' finger knuckle patterns can either be used as independent biometric patterns or employed to improve the performance from the major finger knuckle patterns. This paper investigates a completely automated approach for the `minor' finger knuckle identification by developing steps of region of interest segmentation, image normalization, enhancement and robust matching to accommodate image variations. Comparative experimental results are presented for matching the normalized `minor' finger knuckle images using LBP, ILBP and 1D log Gabor filter. The efforts to develop automated `minor' finger knuckle patterns achieve promising results, with 1.04% equal error rate on the database of 202 subjects, and illustrate its simultaneous use to improve the performance for conventional finger knuckle identification.

In this paper, we proposed a novel finger knuckle pattern (FKP) based personal authentication system using multilayer deep rule based (DRB) classifier. The presented approach is completely data-driven and fully automatic. However, the DRB classifier is generic and can be used in variety of classification or prediction problems. In particular, from the input finger knuckle, two kinds of features (i.e., Binarized Statistical Image Features and Gabor Filer bank) are extracted, which are then fed to fuzzy rules based DRB classifier to determine whether the user is genuine or impostor. Experimental results in the form of accuracy, error equal rate (EER) and receiver operating characteristic (ROC) curves demonstrate that presented DRB classifier is a powerful tool in FKP based biometric identification system. Experiments are reported using publicly available FKP PolyU database provided by University of Hong Kong. Experiments using this database show that the presented framework, in this study, can attain performance better than previously proposed methods. Moreover, score level fusion of all FKP modalities with BSIF + DRB yielded an equal error rate of 0.19% and an accuracy of 99.65%.

This paper describes different approaches for the face authentication from the features and classification abilities point of view. Authors compare two types of features - Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) including their combination. These parameters are classified using Multilayer Neural Network (MLNN) and Support Vector Machines (SVM). Face authentication consists of several steps. The first step contains Viola-Jones algorithm for face detection. Authors resize the detected face for a fixed vector and afterwards, it is converted into grayscale. Next, feature extraction with a simple Min-Max normalization is applied. Obtained parameters are evaluated by classifiers and for each detected face, authors get posterior probability as the output of the classifier. Different approaches for face authentication are compared with each other using False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) curves. The results are verified with AR Face Database and elaborated in a feature extraction and classifier design point of view. Best results were achieved by HOG feature for SVM classifier. Detailed results are listed in the text below.

A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint

Multimodal biometric user authentication using improved decentralized fuzzy vault scheme based on Blockchain network

Biometric recognition is one of the effective authentication techniques which is utilized in various applications for making the individual identification process. During the verification and authentication process different biometric features such as signature, ear, iris, face, palm, finger knuckle details are used to perform this process. Due to the easy acceptance of the palm surface, fine textures and stable features characteristics are helps to choose the finger knuckle feature for biometric process in this work. First the finger biometric features are collected from PolyU finger knuckle database. After that, the noise present in the images are eliminated using weighted median filter and the knuckle region is located with the help of the variational approach. After that key point descriptors are extracted using sparse autoencoder approach. Finally, the specific features are trained using compositional networks and features matching is performed by Chebyshev distance. The matching process authenticate the user whether they are authorized person or not. At last efficiency of the system is evaluated using MATLAB based experimental results such as false acceptance rate, equal error rate and false rejection rate.

In this study, the authors propose a multimodal biometric system by combining the finger knuckle and finger vein images at feature‐level fusion using fractional firefly (FFF) optimisation. Biometric characteristics, like finger knuckle and finger vein are unique and secure. Initially, the features are extracted from the finger knuckle and finger vein images using repeated line tracking method. Then, a newly developed method of feature‐level fusion using FFF optimisation is used. This method is utilised to find out the optimal weight score to fuse the extracted feature sets of finger knuckle and finger vein images. Thus, the recognition is carried out by the fused feature set using layered k‐SVM (k‐support vector machine) which is newly developed by combining the layered SVM classifier and k‐neural network classifier. The experimental results are evaluated and the performance is analysed with false acceptance ratio, false rejection ratio and accuracy. The outcome of the proposed FFF optimisation system obtains a higher accuracy of 96%.

Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques

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.

Existing methods for iris, fingerprint, and 3D face recognition in mobile devices have constraints in terms of price and size owing to their use of additional cameras, lighting, and sensors. Additionally, visible light, camera-based 2D face recognition, palm print recognition, touchless fingerprint recognition, and finger knuckle print recognition are difficult to be used in mobile devices due to limitations in recognition performance and user inconvenience. In response to these problems, studies have been conducted on finger wrinkle recognition in mobile devices; however, image quality is often reduced by motion blurring caused by the movement of the camera or the user's finger, thereby reducing recognition performance. This study proposes a method for restoring and recognizing motion-blurred finger wrinkle images based on a generative adversarial network and deep convolutional neural network. Experiments were performed using two types of finger wrinkle databases, which were custom-made from images of 33 people captured by smart phone cameras (Dongguk mobile finger wrinkle database versions 1 and 2, denoted as DMFW-DB1 and DMFW-DB2, respectively). The results demonstrated high restoration and recognition performance in comparison with the state-of-the-art methods.

This chapters provides methodology for statistical inference concerning receiver operating characteristic (ROCs) curves and equal error rates (EER’). We begin with an introduction to the ROC with special focus on a polar coordinates representation of the ROC. We then propose a new bootstrap methodology for estimation of the variability in a sample ROC. Next we discuss our methodology for making curvewise confidence regions for the ROC. This methodology forms the basis for our approach to inference for the rest of the chapter. Having presented our methodology for a single ROC, we move to methods for comparing two ROC’s. We do this both for the case when the ROC’s are collected independently as well as when the matching scores are collected in a paired manner. Comparisons of three or more ROC’s whether paired or independent is the last topic on ROC’s in this chapter. Our focus then moves to statistical methods for equal error rates (EER’s). Our organization for the part of this chapter on EER’s is similar to our structure for ROC inference. We start with estimation for a single EER. This is followed by methodology for comparing two EER’s and then comparing three or more EER’s. Both sections have descriptions of comparisons for independent and paired data collection. This chapter ends with a further discussion of some of these topics.

This work explores different methods to detect errors in transcriptions of speech recordings. We artificially corrupt well transcribed speech transcriptions with three types of errors: substitution, insertion and deletion on TIMIT phonemic transcriptions and WSJ word transcriptions. First, we use Bayesian model selection method by comparing the log-likelihoods from alignment and phone recognizer, a final score is computed to make decision. In this method, we consider two models, Bayesian Hidden Markov Model (HMM) and a Variational Auto-Encoder (VAE) combined with a HMM. Alternately, we build a biased ASR system with language models trained on individual transcriptions, detection decision is based on Levenshtein distance (LD) between transcription and oracle path from decoded lattice. We evaluate the methods of detecting errors in corrupted TIMIT transcription, the best result (either using model selection with VAE model or biased ASR) achieves 7% equal error rate on the Detection Error Tradeoff (DET) curve; we also evaluate the methods of detecting errors in corrupted WSJ transcriptions, and the best result (using biased ASR) achieves 3% equal error rate.

In this paper, we address the problem of score fusion in biometric authentication. Single valued metrics related to the receiver operating characteristics (ROC) curve, such as Equal Error Rate (EER) and False Rejection Rate (FRR) when False Acceptance Rate equals zero, are extensively used for evaluating biometric authentication performances. Various requirements and preferences, for example, lower EER, or smaller FRR, may be imposed on biometric authentication systems in different application scenarios. We propose a novel method of score fusion based on quasi-convex optimization to directly improve biometric authentication metrics. Experiments based on a face recognition system demonstrate the effectiveness of the proposed method.

Finger Knuckle Print as Unimodal Fuzzy Vault Implementation