Biometric User Identification Research Articles

Modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition Model

Traditional indoor human activity recognition (HAR) is a time-series data classification problem and needs feature extraction. Presently, considerable attention has been given to the domain of HAR due to the enormous amount of its real-time uses in real-time applications, namely surveillance by authorities, biometric user identification, and health monitoring of older people. The extensive usage of the Internet of Things (IoT) and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing. The more commonly utilized inference and problem-solving technique in the HAR system have recently been deep learning (DL). The study develops a Modified Wild Horse Optimization with DL Aided Symmetric Human Activity Recognition (MWHODL-SHAR) model. The major intention of the MWHODL-SHAR model lies in recognition of symmetric activities, namely jogging, walking, standing, sitting, etc. In the presented MWHODL-SHAR technique, the human activities data is pre-processed in various stages to make it compatible for further processing. A convolution neural network with an attention-based long short-term memory (CNN-ALSTM) model is applied for activity recognition. The MWHO algorithm is utilized as a hyperparameter tuning strategy to improve the detection rate of the CNN-ALSTM algorithm. The experimental validation of the MWHODL-SHAR technique is simulated using a benchmark dataset. An extensive comparison study revealed the betterment of the MWHODL-SHAR technique over other recent approaches.

A Hybrid Method for Keystroke Biometric User Identification

The generative model and discriminative model are the two categories of statistical models used in keystroke biometric areas. Generative models have the trait of handling missing or irregular data, and perform well for limited training data. Discriminative models are fast in making predictions for new data, resulting in faster classification of new data compared to the generative models. In an attempt to build an efficient model for keystroke biometric user identification, this study proposes a hybrid POHMM/SVM method taking advantage of both generative and discriminative models. The partially observable hidden Markov model (POHMM) is an extension of the hidden Markov model (HMM), which has shown promising performance in user verification and handling missing or infrequent data. On the other hand, the support vector machine (SVM) has been a widely used discriminative model in keystroke biometric systems for the last decade and achieved a higher accuracy rate for large data sets. In the proposed model, features are extracted using the POHMM model, and a one-class support vector machine is used as the anomaly detector. For user identification, the study examines POHMM parameters using five different discriminative classifiers: support vector machines, k-nearest neighbor, random forest, multilayer perceptron (MLP) neural network, and logistic regression. The best accuracy of 91.3% (mean 0.868, SD 0.132) is achieved by the proposed hybrid POHMM/SVM approach among all generative and discriminative models.

Explaining One-Dimensional Convolutional Models in Human Activity Recognition and Biometric Identification Tasks.

Due to wearables’ popularity, human activity recognition (HAR) plays a significant role in people’s routines. Many deep learning (DL) approaches have studied HAR to classify human activities. Previous studies employ two HAR validation approaches: subject-dependent (SD) and subject-independent (SI). Using accelerometer data, this paper shows how to generate visual explanations about the trained models’ decision making on both HAR and biometric user identification (BUI) tasks and the correlation between them. We adapted gradient-weighted class activation mapping (grad-CAM) to one-dimensional convolutional neural networks (CNN) architectures to produce visual explanations of HAR and BUI models. Our proposed networks achieved 0.978 and 0.755 accuracy, employing both SD and SI. The proposed BUI network achieved 0.937 average accuracy. We demonstrate that HAR’s high performance with SD comes not only from physical activity learning but also from learning an individual’s signature, as in BUI models. Our experiments show that CNN focuses on larger signal sections in BUI, while HAR focuses on smaller signal segments. We also use the grad-CAM technique to identify database bias problems, such as signal discontinuities. Combining explainable techniques with deep learning can help models design, avoid results overestimation, find bias problems, and improve generalization capability.

Compressed-Domain ECG-based Biometric User Identification Using Task-Driven Dictionary Learning

In recent years, user identification has become crucial for authorized machine access. Electrocardiography (ECG) is a new and rising biometrics signature. Rather than traditional biological traits, ECG cannot be easily imitated. In the long-term monitoring system, the wireless wearable ECG biomedical sensor nodes are resource-limited. Recently, compressive sensing (CS) technology is extensively applied to reduce the power of data transmission and acquisition. The prior CS-based reconstruction process aims at improving energy efficiency with different schemes, and they focus on the performance of reconstruction only. Therefore, we present a sparse coding-based classifier, trained by task-driven dictionary learning (TDDL), to realize low-complexity user identification in compressed-domain directly. TDDL is one of the dictionary learning and designed for classification tasks. It co-optimizes the dictionary and classifier weighting simultaneously, which gives better accuracy. In this article, we are proposing a TDDL-based compression learning algorithm for ECG biometric user identification as this directly identifies user identity (ID) without undergoing reconstruction process and conventional classifier. It can extract necessary information from the compressed-ECG signal directly to save the system power and computational complexity. The algorithm has 2%–10% accuracy improvements compared with state-of-the-art algorithms and maintains low complexity at the same time. Our proposed TDDL-CL will be the better choice in the long-term wearable ECG biometric devices.

Human Face Identification using Moments and Transformations

Face recognition one of the most promising techniques such that the importance of biometric user identification is increasing every day, several methods have been suggested to perform it such as classification, deep learning, statistical, moments, etc., this work describes different approaches to develop biometric technique, based on the moments and transformation. The proposed method for face recognition is based on Legendre moments and singular value decomposition for feature extraction. Local approaches by dividing images into several blocks (overlapped blocks and non-overlapped) have been adopted to gain the best recognition rate, as well as normalization using the Z-score method, which has been used. The outcomes experimental showed that the suggested system is effective, it has been tested using ORL face image databases with 10 cases and achieved a recognition rate from 85.27-100%, also, applied on FEI Brazil face database with 10 cases and achieved a recognition rate from to 79.77-100%.

Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models

Currently, a significant amount of interest is focused on research in the field of Human Activity Recognition (HAR) as a result of the wide variety of its practical uses in real-world applications, such as biometric user identification, health monitoring of the elderly, and surveillance by authorities. The widespread use of wearable sensor devices and the Internet of Things (IoT) has led the topic of HAR to become a significant subject in areas of mobile and ubiquitous computing. In recent years, the most widely-used inference and problem-solving approach in the HAR system has been deep learning. Nevertheless, major challenges exist with regard to the application of HAR for problems in biometric user identification in which various human behaviors can be regarded as types of biometric qualities and used for identifying people. In this research study, a novel framework for multi-class wearable user identification, with a basis in the recognition of human behavior through the use of deep learning models, is presented. In order to obtain advanced information regarding users during the performance of various activities, sensory data from tri-axial gyroscopes and tri-axial accelerometers of the wearable devices are applied. Additionally, a set of experiments were shown to validate this work, and the proposed framework’s effectiveness was demonstrated. The results for the two basic models, namely, the Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) deep learning, showed that the highest accuracy for all users was 91.77% and 92.43%, respectively. With regard to the biometric user identification, these are both acceptable levels.

Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis.

Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and compression has been widely applied to reduce the power of data acquisition and transmission. However, prior CS-based frameworks suffer from high reconstruction overhead and cannot directly align compressed ECG signals. In this paper, in order to solve the above two problems, we propose a compressed alignment-aided compressive analysis (CA-CA) algorithm for ECG-based biometric user identification. With CA-CA, it can avoid reconstruction and extract information directly from CS-based compressed ECG signals to reduce overall complexity and power. Besides, CA-CA can also align the compressed ECG signals in the eigenspace-domain, which can further enhance the precision of identifications and reduce the total training time. The experimental result shows that our proposed algorithm has a 94.16% accuracy based on a public database of 22 people.

DeepAirSig: End-to-End Deep Learning Based in-Air Signature Verification

In-air signature verification is vital for biometric user identification in contact-less mode. The state-of-the-art methods use heuristics for signature acquisition, and provide insufficient data to train neural networks for the verification. In this article, we present a novel method for end-to-end deep learning based in-air signature verification using a depth sensor. In this regard, we propose a new medium-scale in-air signature dataset which is created using an accurate convolutional neural network (CNN) based 3D hand pose estimation algorithm. The proposed dataset offers a total of 1800 signatures collected from 40 subjects. So far, dynamic time warping (DTW) has been the most effective and commonly used method for verification. Keeping in view the significant advancement in deep learning, we present a more accurate deep learning based alternative which outperforms DTW by 67.6%. To this end, we train a personalized autoencoder to reconstruct the signature of each subject. Thereafter, the signature is verified by thresholding the reconstruction loss. We perform extensive experiments by formulating spatial and depth features of the signature in images and point clouds based representations. Moreover, for comparisons, we implement several deep learning algorithms (i.e. linear autoencoder, convolutional autoencoder, and Deep One-Class classifier). Our verification approach achieves an EER of 0.055%. The dataset is available at https://bit.ly/2mEJzOw.

Phase-domain Deep Patient-ECG Image Learning for Zero-effort Smart Health Security.

Smart health is quickly boosted by technological advancements: smart sensors, body sensor network, internet of medical things and big data. Vast amounts of smart health big data from ubiquitous sensors pose unprecedented challenges to the security and privacy protection, which is extremely critical in healthcare applications. The vital signs, user daily behaviors, medicine recommendations, and so many other health data are vulnerable to different attacks, due to the fact that wearable/mobile monitors have very strict performance/power constraints, which limit the complexity of security protocols. In this paper, we study how to leverage a natural vital signal (Electrocardiogram - ECG) for user identification purpose, without introducing new hardware sensing devices. ECG is not only a gold standard cardiac signal, but also unique to each individual. We investigate a phase-domain deep patient-ECG image learning framework, to tackle key challenges in ECG biometric user identification: high diversities of ECG morphologies due to heart diseases, and time-consuming/ineffective heartbeat localization methods & manual feature engineering. The ultimate goal is to make the smart health security zero-effort: use `phase-domain transformation' to enable blind signal segmentation without localizing heartbeats; create a computer image processing-like task by `pixelating' phase-domain ECG trajectories to ECG images; and enable automatic (non-manual) `deep feature learning' using a deep convolutional neural network. Evaluated on two patient-ECG databases, this zero-effort framework achieves an accuracy as high as 97.2%, and greatly outperforms state-of-the-art studies in terms of the generalization ability and/or performance. This study is expected to enable highly challenging patient-ECG biometric user identification, by generalizable blind signal segmentation and deep feature learning strategies, in the era of smart health boosted by internet of medical things and big medical data.

On the Influence of Affect in EEG-Based Subject Identification

Biometric signals have been extensively used for user identification and authentication due to their inherent characteristics that are unique to each person. The variation exhibited between the brain signals (EEG) of different people makes such signals especially suitable for biometric user identification. However, the characteristics of these signals are also influenced by the user's current condition, including his/her affective state. In this paper, we analyze the significance of the affect-related component of brain signals within the subject identification context. Consistent results are obtained across three different public datasets, suggesting that the dominant component of the signal is subject-related, but the affective state also has a contribution that affects identification accuracy. Results show that identification accuracy increases when the system has been trained with EEG recordings that refer to similar affective states as the sample that is to be identified. This improvement holds independently of the features and classification algorithm used, and it is generally above 10 percent under a rigorous setting, when the training and validation datasets do not share data from the same recording days. This finding emphasizes the potential benefits of considering affective information in applications that require subject identification, such as user authentication.

A human computer interactions framework for biometric user identification

A human computer interactions framework for biometric user identification

Biometric Identification on the Basis of Facial Features and Modifications Thereof

Biometric user identification is a highly topical theme these days. The most widespread area is identification of a person on the basis of fingerprints, nevertheless it is impossible to overlook other methods, such as scanners of the bloodstream, retina, iris and also identification on the basis of facial features. In the case of scanners which distinguish people according to their facial features, user and functional problems have been demonstrated during testing. One of these problems is the surrounding environment of the scanner device, in particular light conditions. The testing demonstrated that it is necessary to conduct identification of people under laboratory conditions, which is not acceptable from the user perspective. A high error rate was also measured in these systems, both in terms of erroneous acceptance and erroneous rejection of persons. Another highly undesirable factor is the excessively long initial recording of the user template into the system, which is unacceptable for large companies. Due to the conducted tests it was possible to modify the existing technology and design a new method of entering users into the system.

Improvement of the efficiency of data protection by biometric identification

The article briefly considered a relatively new way to protect computer information - the use of biometric user identification. Such method can use a large number of features: features of the work at keyboard, features of the work with the mouse, and others. When implementing a biometric identification method is of interest to compare the value of individual features for recognition. It is proposed to introduce the so-called recognition parameters q, which quantifies the the value of features for individual recognition, as well as to determine the probability of correct recognition on the basis of particular feature or group of features.

An Effective Iris Recognition System Using Fourier Descriptor And Principle Component Analysis

Importance of biometric user identification is increasing every day. One of the most promising techniques is the one based on the human iris. In this work we propose a new method for iris recognition. The proposed system first Detect the iris region using Canny algorithm operator and Hough transform. Then the system normalizes the iris region and removes any noise. The final step in our system is the feature extraction using Fourier Descriptor and Principle Component Analysis (PCA). The result experimentation was carryout out using CASIA database. The experimental results have shown that the proposed system yields attractive performances and could be used for personal identification in an efficient and effective manner.

Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation

Importance of biometric user identification is increasing everyday. One of the most promising techniques is the one based on the human iris. The authors, in this work, describe different approaches to develop this biometric technique. Based on the works carried out by Daugman, the authors have worked using Gabor filters and Hamming distance. But in addition, they have also worked in zero-crossing representation of the dyadic wavelet transform applied to two different iris signatures: one based on a single virtual circle of the iris; the other one based on an annular region. Also other metrics have been applied to be compared with the results obtained with the Hamming distance. In this work Euclidean distance and d Z will be shown. The last proposed approach is translation, rotation and scale invariant. Results will show a classification success up to 99.6% achieving an equal error rate down to 0.12 % and the possibility of having null false acceptance rates with very low false rejection rates.

A peripheral market?

Considering the number of PCs that could benefit from the addition of biometric user identification, the market for biometric PC peripheral devices is potentially massive. But, to date, the number of sales is disappointing, and in some cases is falling. There are many potential drivers and detractors at play, so it may take another few years before the overall dynamics of this market become easier to predict.

Multimodal biometric user-identification system for network-based applications

The authors describe the design and development of a prototype of a multimodal biometric system for the automatic identification of an individual. Unlike in other proposed multimodal biometric systems, biometric features are acquired from the same image, using a low-cost scanner, at the same time. It makes the system suitable for home and for many network-based applications. After the pre-processing phase, the hand-geometry, finger and palm-print features invariant to hand translation and rotation on the scanner are extracted. Fusion at the matching-score level is obtained by means of the total similarity measure. In the decision module, three rules are used to establish identity. The system was tested on a database of 130 persons. The test performance, FAR=0% and FRR=0.2%, suggests that the system can be used in medium/high-security Internet environments.