Mahalanobis Distance Classifier Research Articles

Achieving wheat seedling freezing injury assessment during the seedling stage using Unmanned Ground Vehicle (UGV) and hyperspectral imaging technology

Freezing injury may cause irreversible damage to wheat (Triticum aestivum L) tissues and can significantly reduce yield and quality. Therefore, quick and non-destructively estimating the degree of frost damage for formulating anti-freezing protection strategies and preventing frost damage is very crucial. In this study, we obtained hyperspectral images of wheat leaves for accurate identification of frost damage. A remote-controlled Unmanned Ground Vehicle (UGV) equipped with an imaging spectral camera was used to capture the hyperspectral images of frost-damaged wheat leaves. We compared the efficiency of two methods (the one without removal of weeds, and the other is to remove the corresponding area of weeds from the hyperspectral image by Deeplab V3+) for estimation of wheat freezing damage degree by using four different algorithms; Support Vector Machine Classification (SVM), Mahalanobis Distance Classification (MaD), Minimum Distance Classification (MiD), and Maximum Likelihood Classification (ML). We found that, Deeplab V3+ can efficiently identify the weeds from hyperspectral images, as the overall accuracy (OA) values of different algorithms were high in images with weeds removal as compared to the values in weeds containing images. Further, applying ML model after weeds removal have high OA (93.26 %) as compared to the other models. Thus, using Deeplab V3+ and ML can be a potential approach to identify the freezing injury in wheat for sustainable agricultural productivity.

Digital dual test syphilis/HIV detection based on Fourier Descriptors of Cyclic Voltammetry curves

Background:Effective and timely detection is vital for mitigating the severe impacts of Sexually Transmitted Infections (STI), including syphilis and HIV. Cyclic Voltammetry (CV) sensors have shown promise as diagnostic tools for these STI, offering a pathway towards cost-effective solutions in primary health care settings. Objective:This study aims to pioneer the use of Fourier Descriptors (FDs) in analyzing CV curves as 2D closed contours, targeting the simultaneous detection of syphilis and HIV. Methods:Raw CV signals are filtered, resampled, and transformed into 2D closed contours for FD extraction. Essential shape characteristics are captured through selected coefficients. A complementary geometrical analysis further extracts features like curve areas and principal axes lengths from CV curves. A Mahalanobis Distance Classifier is employed for differentiation between patient and control groups. Results:The evaluation of the proposed method revealed promising results with classification performance metrics such as Accuracy and F1-Score consistently achieving values rounded to 0.95 for syphilis and 0.90 for HIV. These results underscore the potential efficacy of the proposed approach in differentiating between patient and control samples for STI detection. Conclusion:By integrating principles from biosensors, signal processing, image processing, machine learning, and medical diagnostics, this study presents a comprehensive approach to enhance the detection of both syphilis and HIV. This setts the stage for advanced and accessible STI diagnostic solutions.

Quadratic Multilinear Discriminant Analysis for Tensorial Data Classification

Over the past decades, there has been an increase of attention to adapting machine learning methods to fully exploit the higher order structure of tensorial data. One problem of great interest is tensor classification, and in particular the extension of linear discriminant analysis to the multilinear setting. We propose a novel method for multilinear discriminant analysis that is radically different from the ones considered so far, and it is the first extension to tensors of quadratic discriminant analysis. Our proposed approach uses invariant theory to extend the nearest Mahalanobis distance classifier to the higher-order setting, and to formulate a well-behaved optimization problem. We extensively test our method on a variety of synthetic data, outperforming previously proposed MDA techniques. We also show how to leverage multi-lead ECG data by constructing tensors via taut string, and use our method to classify healthy signals versus unhealthy ones; our method outperforms state-of-the-art MDA methods, especially after adding significant levels of noise to the signals. Our approach reached an AUC of 0.95(0.03) on clean signals—where the second best method reached 0.91(0.03)—and an AUC of 0.89(0.03) after adding noise to the signals (with a signal-to-noise-ratio of −30)—where the second best method reached 0.85(0.05). Our approach is fundamentally different than previous work in this direction, and proves to be faster, more stable, and more accurate on the tests we performed.

Computational modelling and simulation to mitigate the risk of daylight exposure in tropical museum buildings

In museum design and operation, daylight is typically discouraged due to high risk of damaging the display objects. However, past studies in high-latitude regions have shown the possibility to apply daylight in museums, so long as it is carefully planned, and the display objects are not from the category of high responsive materials. In the tropical region, the influence of daylighting on light exposure on museum objects is still unknown. This study therefore aims to assess and mitigate the impact of annual daylight exposure on objects with low responsive materials in a tropical daylit museum building. Annual daylight modelling and simulation are performed to achieve the objective, followed with Morris sensitivity analysis and Mahalanobis distance classifier to optimise the outcome. It is found that either WWR or glazing transmissivity gives the greatest influence on the performance indicators. Based on the proposed optimisation algorithm, it is possible to determine the optimum solutions satisfying the performance indicators target, for a certain opening type. Overall, the contribution of this study is the proposed computational modelling and simulation methods to mitigate the exposure risk while optimising daylight as a renewable energy source.

Images Enhancement of Ancient Mural Painting of Bey's Palace Constantine, Algeria and Lacuna Extraction Using Mahalanobis Distance Classification Approach.

As a result of human activity and environmental changes, several types of damages may occur to ancient mural paintings; indeed, lacunae, which refer to the area of paint layer loss, are the most prevalent kind. The presence of lacuna is an essential sign of the progress of mural painting deterioration. Most studies have focused on detecting and removing cracks from old paintings. However, lacuna extraction has not received the necessary consideration and is not well-explored. Furthermore, most recent studies have focused on using deep learning for mural protection and restoration, but deep learning requires a large amount of data and computational resources which is not always available in heritage institutions. In this paper, we present an efficient method to automatically extract lacunae and map deterioration from RGB images of ancient mural paintings of Bey’s Palace in Algeria. Firstly, a preprocessing was applied using Dark Channel Prior (DCP) to enhance the quality and improve visibility of the murals. Secondly, a determination of the training sample and pixel’s grouping was assigned to their closest sample based on Mahalanobis Distance (MD) by calculating both the mean and variance of the classes in three bands (R, G, and B), in addition to the covariance matrix of all the classes to achieve lacuna extraction of the murals. Finally, the accuracy of extraction was calculated. The experimental results showed that the proposed method can achieve a conspicuously high accuracy of 94.33% in extracting lacunae from ancient mural paintings, thus supporting the work of a specialist in heritage institutions in terms of the time- and cost-consuming documentation process.

A Three-Stage Low-Complexity Human Fall Detection Method Using IR-UWB Radar

This paper proposes a novel three-stage low-complexity human fall detection method using an impulse radio ultra-wideband (IR-UWB) radar. The core idea lies in the three cascaded stages, namely large-motion detection, rough fall detection and enhanced fall detection. For the large-motion detection, we assume the fall is a very sparse event in daily life and achieve this by a checking of the high Doppler frequency energy. For the rough fall detection, we do not intuitively determine the fall events, but propose six time-frequency features and two position features, and use the support vector data description (SVDD) detector to divide the large-motions into non-fall and fall-like events. For the enhanced fall detection, we add a new feature and use a Mahalanobis distance classifier to finally determine whether a fall happened. The reasons that two classifiers cascaded instead of one classifier are that (1) we can reduce the difficulty of identifying falls directly from daily events by using a large number of non-fall samples to train the SVDD model for anomaly detection, and allowing a certain false alarm rate; and (2) we can achieve a higher fall detection accuracy in a much smaller searching space by identifying falls only from the fall-like events. Additionally, a real-time edge fall detection system with a commonly used micro control unit is developed. Experiment results show that the proposed method exhibits a low computational complexity, and a relative robustness and high fall detection accuracy under a low false alarm rate.

Klasifikasi Habitat Bentik Berdasarkan Citra Sentinel-2 di Kepulauan Kei, Maluku Tenggara

Imagery classification has long been used in analyzing remote sensing data. The use of the classification algorithm model can affect the results in interpreting benthic habitats in shallow water. This study aimed to determine the best classification algorithm model for mapping benthic habitat cover through Sentinel-2 satellite imagery. Three algorithm models were employed: Maximum Likelihood Classification (MLC), Minimum Distance Classification (MDC), and Mahalanobis Distance Classification (MaDC). The benthic habitat types were extracted using Lyzenga correction, giving three categories: coral, seagrass, and sand. The results showed that the application algorithm models of the MLC, MDC, and MaDC on the benthic habitat mapping resulted in an accuracy value that was not significantly different at the 95% confidence interval. However, of the three algorithms used, the MaDC algorithm provides the best results in overall accuracy (78.35%) than the MDC (74.45%) and the MLC (74.33%). It shows that the MaDC algorithm can be referred to as the mapped benthic habitat cover in the Kei Islands. However, this algorithm model needs to be continuously studied and compared to other models in other locations. Keywords: benthic, habitat classification, Kei Islands, sentinel

Characterizing Prosthesis Control Fault during Human-Prosthesis Interactive Walking Using Intrinsic Sensors.

The physical interactions between wearable lower limb robots and humans have been investigated to inform effective robot design for walking augmentation. However, human-robot interactions when internal faults occur within robots have not been systematically reported, but it is essential to improve the robustness of robotic devices and ensure the user's safety. This paper aims to (1) present a methodology to characterize the behavior of the robotic transfemoral prosthesis as an effective wearable robot platform while interacting with the users in the presence of internal faults, and (2) identify the potential data sources for accurate detection of the prosthesis fault. We first obtained the human perceived response in terms of their walking stability when the prosthesis control fault (inappropriate intrinsic control output/command) was emulated/applied in level-ground walking. Then the measurements and their features, obtained from the transfemoral prosthesis, were examined for the emulated faults that elicited a sense of instability in human users. The optimal features that contributed the most in separating faulty interaction from the normal walking condition were determined using two machine-learning-based approaches: One-Class Support Vector Machine (OCSVM) and Mahalanobis Distance (MD) classifier. The OCSVM anomaly detector could achieve an average sensitivity of 85.7 % and an average false alarm rate of 1.7 % with a reasonable detecting time of 147.6 ms for detecting emulated control errors among all subjects. The result demonstrates the potential of using machine-learning-based schemes in identifying prosthesis control faults based on intrinsic sensors on the prosthesis. This study presents a procedure to study human-robot fault tolerance and inform the future design of robust prosthesis control.

Evaluation of One-Class Classifiers for Fault Detection: Mahalanobis Classifiers and the Mahalanobis–Taguchi System

Today, real-time fault detection and predictive maintenance based on sensor data are actively introduced in various areas such as manufacturing, aircraft, and power system monitoring. Many faults in motors or rotating machinery like industrial robots, aircraft engines, and wind turbines can be diagnosed by analyzing signal data such as vibration and noise. In this study, to detect failures based on vibration data, preprocessing was performed using signal processing techniques such as the Hamming window and the cepstrum transform. After that, 10 statistical condition indicators were extracted to train the machine learning models. Specifically, two types of Mahalanobis distance (MD)-based one-class classification methods, the MD classifier and the Mahalanobis–Taguchi system, were evaluated in detecting the faults of rotating machinery. Their performance for fault detection on rotating machinery was evaluated with different imbalanced ratios of data by comparing with binary classification models, which included classical versions and imbalanced classification versions of support vector machine and random forest algorithms. The experimental results showed the MD-based classifiers became more effective than binary classifiers in cases in which there were much fewer defect data than normal data, which is often common in the real-world industrial field.

Robust generalised quadratic discriminant analysis

Quadratic discriminant analysis (QDA) is a widely used statistical tool to classify observations from different multivariate Normal populations. The generalized quadratic discriminant analysis (GQDA) classification rule/classifier, which generalizes the QDA and the minimum Mahalanobis distance (MMD) classifiers to discriminate between populations with underlying elliptically symmetric distributions competes quite favorably with the QDA classifier when it is optimal and performs much better when QDA fails under non-Normal underlying distributions with heavy tail, e.g. Cauchy distribution. However, the classification rule in GQDA is still based on the sample mean vector and the sample dispersion matrix of a training set, which are extremely non-robust under data contamination. In real world, however, it is quite common to face data which are highly vulnerable to outliers and so the lack of robustness of the classical estimators of the mean vector and the dispersion matrix reduces the efficiency of the GQDA classifier significantly, increasing the misclassification errors. The present paper investigates the performance of the GQDA classifier when the classical estimators of the mean vector and the dispersion matrix used therein are replaced by various robust counterparts. Applications to various real data sets as well as simulation studies reveal far better performance of the proposed robust versions of the GQDA classifier. A comparative study has been made to advocate the appropriate choice of the robust estimators to be used in a specific situation.

A CNN-LSTM based ensemble framework for in-air handwritten Assamese character recognition

In-air handwriting is a contemporary human computer interaction (HCI) technique which enables users to write and communicate in free space in a simple and intuitive manner. Air-written characters exhibit wide variations depending upon different writing styles of users and their speed of articulation, which presents a great challenge towards effective recognition of linguistic characters. So, in this paper we have proposed an ensemble model for in-air handwriting recognition which is based on convolutional neural network (CNN) and a long short-term memory neural network (LSTM-NN). The method collaborates overall character trajectory appearance modeling and temporal trajectory feature modeling for efficient recognition of varied types of air-written characters. In contrast to two-dimensional handwriting, in-air handwriting generally involves writing of characters interlinked by a continuous stroke, which makes segregation of intended writing activity from insignificant connecting motions an intricate task. So, a two-stage statistical framework is incorporated in the system for automatic detection and extraction of relevant writing segments from air-written characters. Identification of writing events from a continuous stream of air-written data is accomplished by formulating a Markov Random Field (MRF) model, while the segmentation of writing events into meaningful handwriting segments and redundant parts is performed by implementation of a Mahalanobis distance (MD) classifier. The proposed approach is assessed on an air-written character dataset comprising of Assamese vowels, consonants and numerals. The experimental results connote that our hybrid network can assimilate more information from the air-writing patterns and hence offer better recognition performance than the state-of-the-art approaches.

Segmentation-Based Adaptive Feature Extraction Combined With Mahalanobis Distance Classification Criterion for Heart Sound Diagnostic System

To utilize heart sound features that may vary according to their suitability for segmentation, automatic adaptive feature extraction combined with the Mahalanobis distance classification criterion is proposed to construct an innovative, heart sound-based system for diagnosing heart diseases. The innovation of this system is primarily reflected in the automatic segmentation and extraction of the first complex heart sound $(\textit {CS}_{1})$ and second complex heart sound $(\textit {CS}_{2})$ or each cardiac sound $(\textit {CS})$ , automatic extraction of the segmentation-based frequency feature ${\mathit {FF}}_{1}$ or $\textit {FF}_{2}$ , determination of the diagnostic features [ $\gamma _{11}$ , $\gamma _{12}$ ] and [ $\gamma _{21}$ , $\gamma _{22}$ , $\gamma _{23}$ ], and the development of a classifier model with adjustable sizes corresponding to the given desired confidence levels (denoted as $\beta $ ). Three stages corresponding to the implementation of the novel diagnostic system are summarized as follows. In stage 1, the time intervals between two sequential peaks are automatically calculated and statistically analyzed, and the result is used to determine whether a given heart sound can be segmented. Stage 2 involves automatic extraction of segmentation-based adaptive features for adapting the heart sound to the frequency domain. Finally, the Gaussian mixture model ( GMM )-based objective function $f_{\textit {et}}(\mathbf {x})$ is generated, and the $k^{th}$ component’s confidence region is determined by adjusting the optimal confidence level $\beta _{k}$ and subsequently used as the classification criterion to diagnose a given heart sound. The performance evaluation was validated with sounds from online heart sound databases and sounds from clinical heart databases. Compared with the state-of-the-art diagnostic methods, the overall accuracy $OA$ of 98.8%, $F_{1}$ of 99.27%, and $\kappa $ of 98.6% are much higher.

Potential Evaluation of High Spatial Resolution Multi-Spectral Images Based on Unmanned Aerial Vehicle in Accurate Recognition of Crop Types

The accurate acquisition of farmland planting information is the basis of precision agriculture. Collecting remote sensing data via unmanned aerial vehicle (UAV) is a convenient method to obtain precision agricultural information because of the high spatiotemporal resolution and flexibility. A quadrotor UAV equipped with a SEQUOIA sensor (one multi-spectral sensor and one RGB lens) was operated over the Jingyuetan agricultural area with five land cover types on September 4, 2017, to investigate the equipment’s feasibility for crop identification. To evaluate the effects of different data and classification methods on the accuracy of crop type classification, three combinations were tested: MDC + Four (Mahalanobis distance classifiers based on four-band reflectance), MDC + VIs (Mahalanobis distance classifiers based on Vegetation Indices) and MLC + VIs (maximum likelihood classifiers based on Vegetation Indices). The accuracy of the different classification methods was 83.06% (MDC + Four), 89.17% (MDC + VIs) and 92.60% (MLC + VIs). The MLC + VIs scheme was the most accurate, as it could partially overcome the influence of shadow and flattened. Since the reflectivity of different bands varied, all kinds of objects on the ground could be distinguished. This result revealed that multi-spectral UAV technology has the potential to identify crop type at the sub-meter spatial resolution, with the MLC based on the VIs.

EVAL Cane: Nonintrusive Monitoring Platform With a Novel Gait-Based User-Identification Scheme

Considering the particularity of fragile people with reduced mobility, we design a nonintrusive platform called EVAL cane to assist and monitor the user’s walking. On the one hand, it has important walking-assistance functions, such as obstacle warning and fall detection. On the other hand, it collects the user’s long-term walking data in the background, which may be the potential physical-health-assessment data. Compared with an ordinary cane, the above two functions are implemented without any burden on the user’s life. In addition, we have considered the authentication security for that the walking data are private. To this end, we propose a novel user-identification scheme, leveraging the user walking gait data collected by EVAL cane. To the best of our knowledge, it is the first time that the gait information collected by cane is used for reinforcing monitoring system security. This scheme does not require the user to remember and enter any identity (such as a password), which is user-friendly for fragile people. In the scheme, a statistics-based rough gait feature-extraction method is put forward at first. Then, in order to improve the identification precision, we design a performance-based feature-deletion (PFD) algorithm to remove the bad features. Finally, a minimum Mahalanobis distance classifier is used. Experimental results show that the user-identification rate without the PFD algorithm can reach as high as 90.48%. In addition, the PFD algorithm further improves the performance by about 6%, reaching an excellent result of 96.43%.

Frequency-Based Optimal Radar Waveform Design for Classification Performance Maximization Using Multiclass Fisher Analysis

In this article, we propose new waveform design procedures and classification schemes to improve target classification performance nonadaptively in radar systems. The new designs and schemes are all inspired by 2-class and multiclass Fisher discriminant analysis. The proposed system does not require as much computational capability as adaptive waveform design systems while also overcoming: 1) angular uncertainty in classifying high fidelity targets and 2) drops in performance experienced by nonadaptive systems when classification is extended to more than two targets. The waveform design procedure is based on an optimization problem to find the waveform that maximizes the objective function inspired by Fisher analysis under constant energy constraint. We also derive two closed-form solutions for the optimization problems under certain conditions for the 2-class and multiclass cases. All the methods are tested using synthetic and real data to show the performance of the proposed methods against the average Mahalanobis distance (AMD) nonadaptive waveform design and classifier.

Total variation on horizontal visibility graph and its application to rolling bearing fault diagnosis

The total variation on graph (TVG) is a powerful vertex domain index for measuring the smoothness of graph signals, but its performance is closely related to the underlying graph. Since the horizontal visibility graph can better reflect the dynamics characteristics of bearing vibration signals than the path graph, the underlying graph of TVG is designated as horizontal visibility graph. The vertex domain index TVG defined on horizontal visibility graph is called simply as TVHVG in this paper. For better distinguishing the different states of rolling bearings, the bearing vibration signal is converted into the graph signal indexed by its horizontal visibility graph, and the vertex domain index TVHVG is extracted as the single fault feature. Based on TVHVG feature extraction and Mahalanobis distance classification, a novel fault diagnosis method for rolling bearings is proposed. The proposed method is applied to analyze two sets of experimental data containing normal and faulty rolling bearings. The results indicate that the proposed method can diagnose the bearing faults with different types and degrees effectively, and the vertex domain index TVHVG is superior to some classical time domain indexes in distinguishing the different states of rolling bearings.

Changes of Wiang Nong Lom and Nong Luang Wetlands in Chiang Saen Valley (Chiang Rai Province, Thailand) During the Period 1988–2017

Pressure on the Wiang Nong Lom and Nong Luang wetland resources in the Chiang Saen Valley of Chiang Rai Province (Thailand) has increased in recent years with the expansion of farmlands and other major sources of wetland conflict related to public land encroachment. Both of these wetlands have been designated as strategic ecosystems. Yet, there is a limited understanding of the way different wetlands respond to change drivers (agriculture, climate, population, etc.), and currently no scientifically valid protocols exist for local wetland mapping and monitoring. Distinguishing between small wetlands and land use and land cover (LULC) components is a challenging affair due to the highly heterogeneous landscape and spectral similarity of compositionally different types of tropical regions. The goals of this article are both technological and substantive, i.e., it aims to (A) propose a synthesis of quantitative techniques that can improve LULC mapping using remotely sensed data (Landsat TM, ETM+, OLI), and (B) assess the wetland changes during the last three decades and better understand the interaction between wetland changes, human population, and the environment. In regards to goal (A), the proposed classification approach employed a synthesis of techniques of decision tree classification (DTC), maximum likelihood classification (MLC), and Mahalanobis distance classification (MDC), with different bands and ancillary data inputs. The results demonstrated that the implementation of the DTC algorithms to address LULC mapping problems exhibited an overall mapping accuracy of 83.9%, which is significantly higher than that of MLC and MDC. It was found that the DTC technique performs well when combined with visible, near-infrared (NIR), and shortwave-infrared bands, a digital elevation model and normalized difference vegetation index layers. Subsequently, the postclassification analysis using DTC showed a notable improvement of approximately 88.0% classification accuracy. Regarding goal (B), our results showed that during the last 30 years, wetland areas in the Chiang Saen Valley have experienced a dramatic decrease of 30.5%, whereas forest landscape surrounding the wetlands has decreased by an astonishing 50.9%. Contrarily, we found that agricultural land size has increased by 24.3%. We suggest that ground data can be linked to the etiology of these results, including the gradual conversion of wetlands to rice cultivation fields as a result of the government rice pledging scheme. Large areas in the study region have been cultivated by para-rubber, palm oil, and pineapple agribusiness production since 2003. In addition, short-term subsidizing government policies promote intensive production for commercial agriculture prompting farmers to transition from subsistence to commercial farming, further impacting wetland conversion. As a result, and in further view of the fact that rapidly expanding agricultural areas have contributed significantly to the decrease of wetland areas during the last three decades, the Chiang Saen Valley wetlands have been designated as wetlands of international importance. The overall recommendation of the present work is that special land-use policy and relevant regulation and/or legislation are critical components of any effort to achieve wetland sustainability.

Performance Analysis of Different Classifier for Remote Sensing Application

The classification of remotely sensed data on thematic map is a challenging task from very long time and it is also a goal of today’s remote sensing because of complexity level of earth surface and selection of suitable classification technique. Hence selection of best classification technique in remote sensing will give better result. Classification of remotely sensed data is an important task within the domain of remote sensing and it is outlined as processing technique that uses a systematic approach to group the pixels into different classes. In this study, we have classified the multispectral data of Udupi district, Karnataka, India using different classifier including Support Vector Machine (SVM), Maximum Likelihood, Minimum Distance and Mahalanobis Distance classifier. The data of dimension 3980x3201 pixels are collected from a Landsat-3 satellite. Performance of the each classifier is compared by conducting accuracy assessment test and Kappa analysis. The obtained results shows that SVM will give accuracy of 95.35% and kappa value of 0.9408 respectively when compared other classifier, hence effectiveness of SVM is a good choice for classifying remotely sensed data.

Segmentation and classification of consumer-grade and dermoscopic skin cancer images using hybrid textural analysis.

We present a skin lesion diagnosis system that segments the lesion and classifies it as melanoma or nonmelanoma. The proposed system is capable to deal with skin lesion images acquired by standard consumer-grade cameras and dermascopes. In order to suppress the image artifacts and enhance the lesion area, we propose an illumination correction strategy which consists of filtering in frequency and spatial domains. We introduce a hybrid model for lesion segmentation, which forms texture segments of the illumination corrected image using a factorization technique. Then based on the texture distinctiveness of the corrected and the texture segmented images, the saliency maps are computed, which are combined to decide lesion texture segments. In order to classify the segmented lesion, we propose a multimodal feature set composed of texture-, shape-, and color-based features. Classification performance of the multimodal features is evaluated using support vector machine, decision trees, and Mahalanobis distance classifiers. We evaluate the performance of the proposed system qualitatively and quantitatively. For the consumer-grade camera skin images dataset and ISIC 2017 dermascopic images dataset, the average segmentation accuracies are 98.4% and 95.4%, respectively; the classification accuracies are 98.06% and 93.95%, respectively.

AUTENTICAÇÃO BIOMÉTRICA PARA SISTEMAS POR MEIO DA DINÂMICA DA DIGITAÇÃO

Biometric authentication through typing dynamics offers security for more systems, coupled with the traditional login and password, as it associates authentication with biometric factors. This work presents methodologies for biometric authentication performance from the middle of the typing dynamics, techniques for extracting characteristics after capturing the time in which to press and release as keys. The methods used were: Mahalanobis distance classifier (classification and authentication) and Random Forest (classification).