Proposed Identification System Research Articles

A robust fingerprint identification approach using a fuzzy system and novel rotation method

Forensic science has developed significantly in the last few decades. Its key role is to provide crime investigators with processed data obtained from the crime scene to achieve more accurate results presented in court. Biometrics has proved its robustness against various critical crimes encountered by forensics experts. Fingerprints are the most important biometric used until now due to their uniqueness and production low cost. The automated fingerprint identification system (AFIS) came into existence in the early 1960s through the cooperation of the countries: USA, UK, France, and Japan. Ever since it started to develop gradually because of the challenges found at the crime scenes such as fingerprints distortions and partial cuts which in turn can severely affect the final calculations made by experts. The vagueness of the results was the main motivation to build a robust fingerprint identification system that introduces new and enhanced methods in its stages to help experts make more accurate decisions. The proposed fingerprint identification system uses Fourier domain analysis for image enhancement, then the system cuts the image around the core point after applying the rotation and core point detection methods. After that, it calculates the similarity based on the distance between fingerprint histograms extracted using the Local Binary Pattern (LBP). The system's last step is to translate the results into a sensible form where it utilizes fuzziness to provide more possibilities for the answer. The proposed identification system showed high efficiency on FVC 2002 and FVC 2000 databases. For instance, the results of applying our system on FVC 2002 provided a set of three ordered matching candidates such that 97.5 % of the results provided the correct candidate as the first order, and the rest of 2.5 % provided the correct candidate as the second order.

Inertial Measurement Unit-Based Frozen Shoulder Identification from Daily Shoulder Tasks Using Machine Learning Approaches.

Frozen shoulder (FS) is a common shoulder condition accompanied by shoulder pain and a loss of shoulder range of motion (ROM). The typical clinical assessment tools such as questionnaires and ROM measurement are susceptible to subjectivity and individual bias. To provide an objective evaluation for clinical assessment, this study proposes an inertial measurement unit (IMU)-based identification system to automatically identify shoulder tasks whether performed by healthy subjects or FS patients. Two groups of features (time-domain statistical features and kinematic features), seven machine learning (ML) techniques, and two deep learning (DL) models are applied in the proposed identification system. For the experiments, 24 FS patients and 20 healthy subjects were recruited to perform five daily shoulder tasks with two IMUs attached to the arm and the wrist. The results demonstrate that the proposed system using deep learning presented the best identification performance using all features. The convolutional neural network achieved the best identification accuracy of 88.26%, and the multilayer perceptron obtained the best F1 score of 89.23%. Further analysis revealed that the identification performance based on wrist features had a higher accuracy compared to that based on arm features. The system's performance using time-domain statistical features has better discriminability in terms of identifying FS compared to using kinematic features. We demonstrate that the implementation of the IMU-based identification system using ML is feasible for FS assessment in clinical practice.

Identification System for Short-Circuit Fault Points in Concentrated Stator Windings of Motors

Motors serve as the primary power sources in a wide range of industrial fields. In recent years, their application has been expanded to electric and hybrid electric vehicles. As the performance of the motors installed in electric vehicles directly affects human life, it is critical to diagnose the condition of the windings. The objective of this article is to establish a method to identify the short-circuit fault points in concentrated stator windings based on the magnetic flux density distribution near the stator windings. Unlike with distributed windings, the coils are wound around the teeth in concentrated windings. Thus, it is expected that the accurate position specification of the short circuit can be realized if a detailed magnetic flux density distribution over the teeth is obtained with an appropriate magnetic field sensor. The problem of sensor positioning is solved with two stepper motors moving the search coil in the rotational and longitudinal directions independently at specified intervals. The excellent capability of the proposed system is verified through experiments using the stator winding employed in hybrid electric vehicles. The accuracy and sensitivity of the proposed identification system for short-circuit fault points may enable its practical application in industries, for example, shipping and periodic inspections as well as the production management of motors with concentrated stator windings.

Modified Meta Heuristic BAT with ML Classifiers for Detection of Autism Spectrum Disorder.

ASD (autism spectrum disorder) is a complex developmental and neurological disorder that impacts the social life of the affected person by disturbing their capability for interaction and communication. As it is a behavioural disorder, early treatment will improve the quality of life of ASD patients. Traditional screening is carried out with behavioural assessment through trained physicians, which is expensive and time-consuming. To resolve the issue, several conventional methods strive to achieve an effective ASD identification system, but are limited by handling large data sets, accuracy, and speed. Therefore, the proposed identification system employed the MBA (modified bat) algorithm based on ANN (artificial neural networks), modified ANN (modified artificial neural networks), DT (decision tree), and KNN (k-nearest neighbours) for the classification of ASD in children and adolescents. A BA (bat algorithm) is utilised for the automatic zooming capability, which improves the system's efficacy by excellently finding the solutions in the identification system. Conversely, BA is effective in the identification, it still has certain drawbacks like speed, accuracy, and falls into local extremum. Therefore, the proposed identification system modifies the BA optimisation with random perturbation of trends and optimal orientation. The dataset utilised in the respective model is the Q-chat-10 dataset. This dataset contains data of four stages of age groups such as toddlers, children, adolescents, and adults. To analyse the quality of the dataset, dataset evaluation mechanism, such as the Chi-Squared Statistic and p-value, are used in the respective research. The evaluation signifies the relation of the dataset with respect to the proposed model. Further, the performance of the proposed detection system is examined with certain performance metrics to calculate its efficiency. The outcome revealed that the modified ANN classifier model attained an accuracy of 1.00, ensuring improved performance when compared with other state-of-the-art methods. Thus, the proposed model was intended to assist physicians and researchers in enhancing the diagnosis of ASD to improve the standard of life of ASD patients.

Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data

With the increasing popularity of smartphones, user identification has become a critical component to ensure security and privacy. This study looked into how smartphone sensors’ data can be utilized to identify/authenticate users and gives suggestions for the best application components. A public smartphone dataset was used to train a deep learning algorithms, conventional classifiers, and voting classifiers, which were then used to identify the users. Feature selection and Pre-processing techniques were investigated to improve the performance. According to the results, Recursive Feature Elimination beat the other feature-selection approaches, and Long Short-Term Memory (LSTM) had the best identification performance, as evidenced by a relatively large number of machine learning performance metrics. Even with a larger number of users, the proposed identification system performed well and outperformed existing approaches, which were primarily designed and tested on the same public smartphone dataset. In terms of user authentication, this study compared the effectiveness of accelerometer data against gyroscope data. According to the findings, the accelerometer data surpassed the gyroscope data in the authentication process. Notably, the study revealed that employing LSTM to combine the accelerometer and gyroscope data resulted in near-perfect user authentication. The insights gained from this study help to develop user identification and authentication approaches that employ smartphone accelerometer data.

The Feasibility of Human Identification from Multiple ECGs using Maximal Overlap Discrete Wavelet Transform (MODWT) and Weighted Majority Voting Method (WMVM)

Electrocardiography (ECG) has been a subject of research interest in human identification because it is a promising biometric trait that is believed to have discriminatory characteristics. However, features of ECGs that are recorded at different times are often likely to vary significantly. To address the variability of ECG features over multiple records, we propose a new methodology for human identification using ECGs recorded on different days. To demonstrate the applicability of our method, we use the publicly available ECG ID dataset. The main goal of this work is to extract the most significant and discriminative wavelet components of the ECG signal, followed by utilizing the ECG spectral change for human identification using multi-level filtering technique. Our proposed multi-channel identification system is based on using the Maximal Overlap Discrete Wavelet Transform (MODWT) and its inverse (the IMODWT) to create multiple filtered ECG signals. The discriminative feature that we utilize for human identification is based on modeling the dynamic change of the frequency components in these multiple filtered signals. To reach the best possible identification performance, we use the Weighted Majority Voting Method (WMVM) for ECG classification. We evaluated the robustness of our proposed method over several random experiments and obtained 92.29% average identification accuracy, 0.9495 precision, 0.9229 recall, 0.0771 FRR and 0.0013 FAR. These results indicate that filtering some of the ECG wavelet components along with performing data fusion technique can be utilized for human identification.

Driver Identification System Using 2D ECG and EMG Based on Multistream CNN for Intelligent Vehicle

As autonomous driving technologies develop, biometrics technology using driver's bio-signals provides various driver-customized infotainment services. Many studies on driver identification are underway to improve the feature extraction and classification steps to identify drivers with high accuracy. The existing identification systems have used a driver's single biological signal to identify the driver without considering the driver's driving state. The identification error rate was high because electromyography (EMG) is included according to motion artifacts while driving in the electrocardiogram (ECG) acquired by the driver's behavioral characteristics. In this letter, EMG acquired by the driver's motion and ECG acquired by the behavioral characteristics are used simultaneously to improve the identification accuracy. The proposed identification system converts ECG and EMG into 2D constant Q transform (CQT) images and classifies drivers by multistream convolutional neural network (CNN). Our extensive experiments show that a single ECG can achieve 98.1% of the identification accuracy, a single EMG can achieve 84.4% of the identification accuracy, and a multisignal can achieve 98.9% of the identification accuracy with 2D CQT.

SIMULTANEOUS PARAMETRIC IDENTIFICATION FOR THE COUPLED RELAXATION GENERATORS SYSTEM

This paper is devoted to the identification system creation for the coupled relaxation generators system. This nonlineear system can demonstrate both complex-periodic and chaotic behaviour. Proposed identification system, unlike previous one, must be capable to conduct simultaneous identification of the set of parameters. Identification method is based on the moving average and regression analysis methods hybridization. This method is capable to negate the noise impact after differentiation. Special lock and reset system block accumulators during relaxation pulses. The workability and dynamic properties was researched on real equipment. Dynamic characteristics of the method under consideration appears to be sufficient to track parameters values both in complex-periodic or chaotic modes.

A hybrid artificial neural network-based identification systemfor fine-grained composites

Recent interest in the development of innovative building materials has brought about the need for a detailed assessment of their mechanical fracture properties. The parameters for these need to be acquired, and one of the possible ways of doing so is to obtain them indirectly - based on a combination of fracture testing and inverse analysis. The paper describes a method for the identification of selected parameters of mortars and other fine-grained brittle matrix composites. The cornerstone of the method is the use of an artificial neural network, which is utilized as a surrogate model of the inverse relation between the measured specimen response parameters and the sought material parameters. Due to the potentially wide range of composite mixtures and hence the wide range of experimental responses likely to be gained from individual specimens, an ensemble of artificial neural networks was created. It allows the entire range of variants to be covered and provides resulting parameter values with sufficient precision. Such a system is also easy to expand if a composite with properties outside the current range is tested. The capabilities of the proposed identification system are demonstrated on two selected types of fine-grained composites with different specimen responses. The first group of specimens was made of composite based on alkali-activated slag with standardized and natural sand investigated within the time interval of 3 to 330 days of aging. The second tested composite contained alkali-activated fly ash matrix, and the effect of the addition of natural fibers on fracture response was investigated.

Dorsal Hand Veins Based Biometric Identification System Using Deep Learning

Identification systems have become biometric-based, especially with the increase in the performance rates of machine learning methods. Biometric identification systems offer a high level of security by using reliable, difficult-to-change parameters. In this thesis, a biometric identification system is proposed using dorsal hand vein patterns. The relevant system has been tested on the sample dataset in the literature. The number of data were increased by adding noisy data to the data set used. The classification was made on the preprocessed images using SVM, ANN, LDA + KNN, and CNN methods. It has been determined that the highest identification accuracy is achieved when CNN is used, and CNN method provides higher performance compared to other methods. With the proposed identification system, after multiple runs, an average accuracy of 99.64% is achieved with the CNN machine learning method.

Enhancing the identification accuracy of deep learning object detection using natural language processing

In recent years, object detection technology with artificial intelligence has been applied in many fields. This study uses a deep learning method to train an identification model to classify and browse pictures of the 600 different kinds of birds in Taiwan. To enhance the accuracy of identification and classification of these birds, we propose an automatic extraction system that can obtain training data by visiting public social media pages. We also develop mobile apps that allow users to take pictures of birds and upload them to an identification server to enable real-time identification and provide training data. These mobile apps are sent candidate bird pictures by the identification server to allow users to confirm and give feedback when the confidence level of identification is within a critical range. The bird pictures are then used as training data, and the identification model is periodically retrained to optimise the model. We also use natural language processing technology to enhance the level of confidence in image identification. The features of the birds’ appearance are described in words and candidate birds are obtained through image identification and used to readjust the adopted weight values. The proposed identification system gives a relatively high identification accuracy due to the use of deep learning object detection.

An Adaptive Identification of Rotor Time Constant for Speed-Sensorless Induction Motor Drives: A Case Study for Six-Phase Induction Machine

Accurate rotor time constant ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{r}$ </tex-math></inline-formula> ) value is necessary to ensure an acceptable performance of the indirect field-oriented control (IFOC) strategy, where a detuned <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{r}$ </tex-math></inline-formula> affects rotor flux orientation and, in turn, results in poor dynamic and steady-state response of torque. The problems associated with detuned <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{r}$ </tex-math></inline-formula> will be exacerbated in sensorless drives when a model-based speed estimation algorithm is employed. This article investigates the problem of simultaneous identification of the rotor time constant and rotor speed for sensorless IFOC of six-phase induction motor (6PIM) drives. First, an adaptive observer is proposed for online detection of the low-frequency sinusoidal signal, which is intentionally injected into the rotor flux command. Then, a novel model reference adaptive system (MRAS)-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{r}$ </tex-math></inline-formula> estimator is proposed using the detected signal. The Lyapunov stability theorem is used to ensure the asymptotic stability of the proposed identification system. The proposed method is based on the induction machine (IM) model in primary subspace, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> subspace. Hence, it is usable for other multiphase IM (three phases and higher). Nevertheless, the 6PIM is adopted here as a case study. The simulation and experimental results clarify the effectiveness of the proposed parallel estimation system.

A new proposal for automatic identification of multiple soybean diseases

This work proposes a combination of digital image processing techniques consisting of color moments, local binary patterns (LBP) and bag of visual words (BoVW) model for automatic detection of soybean diseases based on the analysis of color, texture and local characteristics of spots on affected leaves. The characteristics extracted after data collection and the application of the cited techniques are grouped and used as input to the support vector machine (SVM) for the purpose of disease classification. The images used in the development and validation of the proposed identification system were obtained from Digipathos, a database provided by the Brazilian Agricultural Research Agency (Embrapa), consisting of 354 images related to 8 typical soybean diseases, namely, bacterial blight, soybean rust, copper phytotoxicity, soybean mosaic, target spot, downy mildew, powdery mildew and Septoria brown spot. Performance analyses are presented separately for the techniques used in several stages of data processing and for the overall system consisting of the combination of these techniques and the SVM classifier. The proposed system achieves an success rate of 75.8%, representing an improvement of approximately 17% over results of methods in the existing literature.

ECG-Based Subject Identification Using Common Spatial Pattern and SVM

In this paper, a nonfiducial electrocardiogram (ECG, the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin) identification system based on the common spatial pattern (CSP) feature extraction technique is presented. The single- and multilead ECG signals of each subject are divided into nonoverlapping segments, and different segment lengths (1, 3, 5, 7, 10, or 15 seconds) are investigated. Features are extracted from each signal segment through projection on a CSP projection matrix. The extracted features are then used to train a radial basis function kernel-based Support Vector Machine (SVM) classifier, which is then employed in the identification phase. The proposed identification system was evaluated on 10, 20, …, 200 reference subjects of the Physikalisch-Technische Bundesanstalt (PTB) ECG database. Using a single limb-based lead (I) with 200 reference subjects, the system achieved an identification rate of 95.15% and equal error rate of 0.1. The use of a single chest-based lead (V3) for 200 reference subjects resulted in an identification rate of 98.92% and equal error rate of 0.08.

Human identification using a new matching Pursuit-based feature set of ECG

Background and ObjectiveIn recent years, many attempts have been made to design reliable systems for identifying individuals using biometrics. Electrocardiogram (ECG) biometric is one of the newest methods that not only offers unique characteristics of individuals for human identification, but also the possibility of counterfeiting it is negligible. In this paper, our objective was to develop an identification system using a non-fiducial one-lead ECG feature set based on a sparse algorithm. MethodsThe ECG signals of 90 participants were decomposed using a matching pursuit (MP) and several statistical and nonlinear measures were extracted from the MP coefficients. Then, the performance of ECG characteristics delivered by MP analysis in human identification was evaluated by the probabilistic neural network (PNN) and k-nearest neighbor (kNN) with one vs. all strategy. The role of the feature set in classification rates was also tested in different modes, including linear attributes, nonlinear indices, all features, features selected by principal component analysis (PCA), and features selected by linear discriminant analysis (LDA). ResultsExperimental results showed that (1) the highest recognition rate was 99.68%; (2) the performance of the PNN was superior to the kNN; and (3) selecting features with LDA resulted in higher identification rates. ConclusionsThe results are prominent from the performance perspective because it gives higher recognition rates over the group of 90 participants. The great performance of the proposed identification system advocates that it can be employed confidently in different smart systems.

Component-connected Feature for Signature Identification

A signature is the oldest security techniques to verify the identification of a person. This is due to every person has a different signature and each signature has the characteristic physiological and behavior. There are two kinds of signature such as offline and online signatures used to verify someone identity. Offline signatures were used in this study because offline signature does not have dynamic features such as an online signature. This study proposed an identification system of offline signature by using k-NN based on the features that were stored in the database. The proposed identification system consists of preprocessing, feature extraction and verification stages. We collected the data samples from 10 persons. Each person wrote 10 signatures. Total data was 100 signatures. The first stage used in this study was preprocessing such as noise removal, binarization, skeleton, and cropping. The second stage was feature extraction. Feature extraction had some important information such as height-width ratio, the ratio of the density of signatures, edge distance ratio, the ratio of the number and proximity of the column, and the number of connected components in the signature. That information was stored in a separate database. We separated 10 signatures of each person into 6 signatures as data sample and 4 signature as test data. We verified 40 signatures of test data from 10 persons using k-NN. It is shown that from 40 signatures used in our test data, 28 signatures were correctly identified and 12 signatures belong to others.

Development of intelligent sorting system realized with the aid of laser-induced breakdown spectroscopy and hybrid preprocessing algorithm-based radial basis function neural networks for recycling black plastic wastes

Plastic recycling has been the key issue for reducing environmental problems and resolving resource depletion. To improve the recovery rate of plastics, the plastic wastes are correctly identified according to their resin type. However, the identification system, which is able to identify black plastics according to not only the type of black plastics but also the grade of resins correctly, has not been introduced. In this paper, laser-induced breakdown spectroscopy, intelligent algorithms and preprocessing algorithms are used to improve the identification of black plastics such as polypropylene, polystyrene (PS), and acrylonitrile butadiene styrene (ABS). The laser-induced breakdown spectroscopy is capable of obtaining the characteristic spectrum regardless of material’s physical state. To extract the new features which are very valuable to improving learning performance, increasing computational efficiency, and building better generalization models from the obtained spectra through laser-induced breakdown spectroscopy, the hybrid preprocessing algorithm, composed of principal component analysis and independent component analysis, is used. In addition, the intelligent algorithm named the extended radial basis function neural networks inheriting the advantages of fuzzy theory and neural networks is used to identify black plastic samples into several categories with respect to their resins. The proposed identification system, composed of three parts such as laser induced breakdown spectroscopy, hybrid preprocessing algorithms, and an efficient intelligent classification algorithm, is able to show the synergy effect on the black plastic identification problem. From several experimental results, it can be seen that the identification system based on laser-induced breakdown spectroscopy and the intelligent algorithm is used for identification of black plastics by resin type.

Sea state identification based on vessel motion response learning via multi-layer classifiers

In order to extend the operational weather window for marine vessels under Dynamic Positioning (DP) control, a novel sea state identification method with multi-layer classifiers is proposed in this paper. Due to the distinction of system responses for various sea states, four motion signals including surge, sway, roll and yaw are adopted for classification purpose. Firstly, preprocessing techniques, like filtration and k-means clustering are performed to the raw data to filter out the “corrupted” low frequency (LF) information and generate the band-pass filter bank. Then, the processed data is decomposed into 20 categories via Hilbert-Huang transform (HHT), filter bank method and wavelet transform and 11 statistical features are extracted for each category. Subsequently, Max-relevance Min-redundancy (mRMR) method helps to select salient features with best trade-off between relevance and redundancy. With these selected features, a newly developed three-layer classification structure with Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest (RF) and Particle Swarm Optimization (PSO) based combination classifiers is proposed to derive the current sea state. The simulation results demonstrate that the proposed identification system can achieve satisfactory classification accuracy. Moreover, the multi-layer classifier outperforms single layer classifier and can rapidly classify the sea state in real-time implementation.

Enhancing Face Identification Using Local Binary Patterns and K-Nearest Neighbors

The human face plays an important role in our social interaction, conveying people’s identity. Using the human face as a key to security, biometric passwords technology has received significant attention in the past several years due to its potential for a wide variety of applications. Faces can have many variations in appearance (aging, facial expression, illumination, inaccurate alignment and pose) which continue to cause poor ability to recognize identity. The purpose of our research work is to provide an approach that contributes to resolve face identification issues with large variations of parameters such as pose, illumination, and expression. For provable outcomes, we combined two algorithms: (a) robustness local binary pattern (LBP), used for facial feature extractions; (b) k-nearest neighbor (K-NN) for image classifications. Our experiment has been conducted on the CMU PIE (Carnegie Mellon University Pose, Illumination, and Expression) face database and the LFW (Labeled Faces in the Wild) dataset. The proposed identification system shows higher performance, and also provides successful face similarity measures focus on feature extractions.

Biometrics System based Human Identification using STR DNA Marker

Human identification plays an important role in numerous fields in the world, such as forensic, government institution, medical application, ..etc. Biometric is a metric to measure the biological characteristics that has then been used for identification and verification functions. The verification process is performed by compare the biometric characteristic such as, fingerprint, iris, DNA, etc., with a pre-stored record, while the identification process done by find the best match between the biometric characteristic and all records saved in a database. In this paper, we propose a biometrics system based on the DNA as a biometric technology for human identification using sixteen Short Tandem Repeats (STRs) DNA marker. A database of 139 records has been considered for Iraqi Diyala Province Population as a real data. In addition, a million DNA profiles has been generated randomly to test the performance of the proposed system. A database has been built using SQL SERVER software environment that provides a high efficiency in human identification. The proposed identification system introduces different search and matching methods with distinct matching level ratios that ease the utilization by users. The outcome results of the proposal system show a flexibility in term of inserting, searching, updating and a high ratio of matching. General Terms Bio-metrics System, DNA fingerprint.