Multi-class Linear Support Vector Machine Research Articles

A Secure and Efficient Biometric Template Protection Scheme for Palmprint Recognition System

The accuracy and security of a biometric system are the two sides of a coin. A biometric system must be simple, flexible, efficient, and secure enough from unauthorized access. Concerning these requirements, this paper presents an enhanced palmprint recognition system with superior performance and improved biometric template protection schemes. This method comprises five components: image preprocessing, feature extraction, cancelable biometrics, classification, and Bio-cryptosystem. The palm texture region is extracted from the input hand image during preprocessing. A feature representation technique is then used to extract discriminant features from the palmprint texture patterns. The extracted features are then analyzed using an information coding scheme to compute a user-specific token. Here, the user-specific tokens along with the original palmprint features are used to calculate the feature vectors for the cancelable biometric system. A novel Bio-Cryptosystem has been proposed to protect templates with high security where there is no need to remember the users&#x2019; tokens. Cancelable feature vectors are kept online in encrypted form using the proposed Bio-Cryptosystem, which is used to identify or verify the subjects after decryption. The original feature vectors are kept offline to prevent these from external attacks and misuse. Finally, the multi-class linear support vector machine has been used for user classification. For experimental purposes, two benchmark hand image databases: BOSPHORUS and CASIA-Palmprint, are employed, and the proposed system has achieved nearly 100&#x0025; correct recognition rate in cancelable domain for both the employed databases. Finally, the performance comparison in terms of both verification and identification for the palmprint and the cancelable palmprint recognition systems with the existing state-of-the-art methods shows the superiority of the proposed system. <i>Impact Statement</i> &#x2014;We have improved the biometric template protection scheme by modifying the cancelable biometric technique with two security levels capable of protecting the biometric information from the dictionary, brute-force, database, channel, replay, and spoofing attacks. To avoid the difficulty of remembering long user passwords/tokens, an automatic key generation technique of user passwords has been proposed. Two different keys are generated: the first one used for cancelable biometrics and the second for encryption-decryption processes. Both these keys are not needed to store or remember and are helpful for a multi-factor authentication system. A novel Bio-cryptographic algorithm instead of traditional Bio-Cryptographic algorithms have been introduced within the proposed system. Several attacks and challenging issues of biometric recognition systems have been considered with maintaining outstanding performance in terms of accuracy and security.

A Facial Expression Recognition Approach for Social IoT Frameworks

Social IoT has become a sensitive topic in the last years, mainly due to the attraction of social networks and the related digital activities amongst the population. These techniques are gaining even more importance in the current period, in which digital tools are the only ones allowed to maintain social distancing due to the COVID-19 restrictions. In order to aid patients and elderly people in-home healthcare context, this article explores the usage of facial patient images and emotional detection. In this regard, a Social IoT approach is proposed, which is based on a camera connected home, allowing medical examinations at a distance by keeping posted the preferred contacts of the patient. A facial expression analysis is done to infer the patient's emotional state, thus communicating to the doctor and the emergency contacts any change in the patient's state (pain, suffering, etc.). The proposed facial expression recognition system consists of three main steps: during the image preprocessing phase, face detection and normalization are performed; the feature extraction process involves the computation of discriminative patterns using the Spatial Pyramid Technique; finally, an expression recognition model is built using a multi-class linear Support Vector Machine classifier. The performance of the proposed system has been tested on two challenging benchmarks for facial expression recognition, namely KDEF and GENKI-4K, which show that the proposed system overcomes state-of-the-art methods.

Multi-Class Brain Disease Classification Using Modified Pre-Trained Convolutional Neural Networks Model with Substantial Data Augmentation

The integration of various algorithms in the medical field to diagnose brain disorders is significant. Generally, Computed Tomography, Magnetic Resonance Imaging techniques have been used to diagnose brain images. Subsequently, segmentation and classification of brain disease remain an exigent task in medical image processing. This paper presents an extended model for brain image classification based on a Modified pre-trained convolutional neural network model with extensive data augmentation. The proposed system has been efficiently trained using the technique of substantial data augmentation in the pre-processing stage. In the first phase, the pre-trained models namely AlexNet, VGGNet-19, and ResNet-50 are employed to classify the brain disease. In the second phase, the idea of integrating the existing pre-trained model with a multiclass linear support vector machine is incorporated. Hence, the SoftMax layer of pre-trained models is replaced with a multi class linear support vector machine classifier is proposed. These proposed modified pre-trained model is employed to classify brain images as normal, inflammatory, degenerative, neoplastic and cerebrovascular diseases. The training loss, mean square error, and classification accuracy have been improved through the concept of Cyclic Learning rate. The appropriateness of transfer learning has been demonstrated by applying three convolutional neural network models, namely, AlexNet, VGGNet-19, and ResNet-50. It has been observed that the modified pre-trained models achieved a higher classification rate of accuracies of 93.45% when compared with a finetuned pre-trained model of 89.65%. The best classification accuracy of 92.11%, 92.83% and 93.45% has been attained in the proposed method of the modified pre-trained model. A comparison of the proposed model with other pre-trained models is also presented.

Deep Brain Stimulation Can Differentiate Subregions of the Human Subthalamic Nucleus Area by EEG Biomarkers.

Introduction: Precise lead localization is crucial for an optimal clinical outcome of subthalamic nucleus (STN) deep brain stimulation (DBS) treatment in patients with Parkinson's disease (PD). Currently, anatomical measures, as well as invasive intraoperative electrophysiological recordings, are used to locate DBS electrodes. The objective of this study was to find an alternative electrophysiology tool for STN DBS lead localization.Methods: Sixty-one postoperative electrophysiology recording sessions were obtained from 17 DBS-treated patients with PD. An intraoperative physiological method automatically detected STN borders and subregions. Postoperative EEG cortical activity was measured, while STN low frequency stimulation (LFS) was applied to different areas inside and outside the STN. Machine learning models were used to differentiate stimulation locations, based on EEG analysis of engineered features.Results: A machine learning algorithm identified the top 25 evoked response potentials (ERPs), engineered features that can differentiate inside and outside STN stimulation locations as well as within STN stimulation locations. Evoked responses in the medial and ipsilateral fronto-central areas were found to be most significant for predicting the location of STN stimulation. Two-class linear support vector machine (SVM) predicted the inside (dorso-lateral region, DLR, and ventro-medial region, VMR) vs. outside [zona incerta, ZI, STN stimulation classification with an accuracy of 0.98 and 0.82 for ZI vs. VMR and ZI vs. DLR, respectively, and an accuracy of 0.77 for the within STN (DLR vs. VMR)]. Multiclass linear SVM predicted all areas with an accuracy of 0.82 for the outside and within STN stimulation locations (ZI vs. DLR vs. VMR).Conclusions: Electroencephalogram biomarkers can use low-frequency STN stimulation to localize STN DBS electrodes to ZI, DLR, and VMR STN subregions. These models can be used for both intraoperative electrode localization and postoperative stimulation programming sessions, and have a potential to improve STN DBS clinical outcomes.

Detecting Unsafe Conditions of a Lathe using an Artificial Neural Network with Three-axis Acceleration Data

Detecting unsafe conditions of a lathe is critical to prevent hazards in a workplace. This study proposed an artificial neural network (ANN) model to classify the state of a lathe into one of the nine conditions (two normal conditions and seven unsafe conditions) based on three-axis acceleration data. The two normal conditions were (1) idle and (2) normal processing. The seven unsafe conditions included unsafe states of a lathe (i.e., eccentric rotation, chipping, improper workpiece fixation, and base looseness) and a worker (i.e., glove contact, hair contact, and necklace contact). The acceleration data for each condition were measured for 30 s using a small lathe and smoothed with the moving average. The datasets were randomly divided into three different sets for training (70%), validation (15%), and testing (15%). The ANN model was trained using the training and validation sets and its performance was evaluated using the testing set. The testing results showed that the classification accuracy of the ANN model proposed in this study (100%) was better than that of a multiclass linear support vector machine model (68%). The procedure and the ANN model established in this study can be utilized to detect unsafe conditions of a lathe and other industrial machines.

Voice Signal Typing Using a Pattern Recognition Approach

Voice signal classification in three types according to their degree of periodicity, a task known as signal typing, is a relevant preprocessing step before computing any perturbation measures. However, it is a time consuming and subjective activity. This has given rise to interest in automatic systems that use objective measures to distinguish among the different signal types. The purpose of this paper is twofold. First, to propose a pattern recognition approach for automatic voice signal typing based on a multi-class linear Support Vector Machine, and using rather well-known parameters like Jitter, Shimmer, Harmonic-to-Noise Ratio, and Cepstral Prominence Peak in combination with nonlinear dynamics measures. Two novel features are also proposed as objective parameters. Second, to validate this approach using a large amount of signals coming from two well-known corpora using cross-dataset experiments to assess the generalizability of the system. A total amount of 1262 signals labeled by professional voice pathologists were used with this purpose. Statistically significant differences between all types were found for all features. Accuracies over 82.71% were estimated in all intra-datasets and inter-datasets using cross-validation. Finally, the use of posterior probabilities is proposed as a measure of the reliability of the assigned type. This could help clinicians to make a more informed decision about the type assigned to a voice. These outcomes suggest that the proposed approach can successfully discriminate among the three voice types, paving the way to a fully automatic tool for voice signal typing in the future.

Face recognition using fusion of feature learning techniques

A method for face recognition system for both challenging frontal and profile faces is proposed in this paper. The proposed system consists of face pre-processing, feature extraction and classification components. During pre-processing, a region-of-interest for face region is extracted based on facial landmark points, obtained by a Tree Structured Part Model. During feature extraction, Scale Invariant Feature Transform descriptors are computed from patches over detected face region. These descriptors undergo to different feature learning techniques to obtain different feature representations for the input image. The performance of these feature representations are obtained using multi-class linear Support Vector Machine classifier during classification. Finally, the scores from different feature learning techniques are fused to take the decision to recognize the subjects. Extensive experimental results have been demonstrated to show the effectiveness of the proposed face recognition system. The comparison with the exiting state-of-art methods for ORL, IITK, CVL, AR, CASIA-Face-V5, FERET and CAS-PEAL face databases, show the superiority of the proposed system.

Effect of fusing features from multiple DCNN architectures in image classification

Automatic image classification has become a necessary task to handle the rapidly growing digital image usage. It has branched out many algorithms and adopted new techniques. Among them, feature fusion-based image classification methods rely on hand-crafted features traditionally. However, it has been proven that the bottleneck features extracted through pre-trained convolutional neural networks (CNNs) can improve the classification accuracy. Thence, this study analyses the effect of fusing such cues from multiple architectures without being tied to any hand-crafted features. First, the CNN features are extracted from three different pre-trained models, namely AlexNet, VGG-16, and Inception-V3. Then, a generalised feature space is formed by employing principal component reconstruction and energy-level normalisation, where the features from individual CNN are mapped into a common subspace and embedded using arithmetic rules to construct fused feature vectors (FFVs). This transformation play a vital role in creating a representation that is appearance invariant by capturing complementary information of different high-level features. Finally, a multi-class linear support vector machine is trained. The experimental results demonstrate that such multi-modal CNN feature fusion is well suited for image/object classification tasks, but surprisingly it has not been explored so far by the computer vision research community extensively.

Human Action Recognition Utilizing Variations in Skeleton Dimensions

This paper presents a human action recognition system that distinguishes between different actions using a new set of features based on global variation in the visual appearance of the subject body. The proposed technique utilizes the changes in human body dimensions, during performing an action, to extract this feature set. These dimension variations are calculated from the human body skeleton performing the action to be recognized. The technique can process both 2D and 3D camera videos due to the high-level nature of the extracted features where the skeleton can be extracted from a video captured using traditional 2D cameras or depth sensing cameras. Finally, a multi-class linear support vector machine is employed in the classification stage. Experiments are conducted on Weizmann, Berkeley MHAD, and MSR-Action3D datasets. The results show that the technique achieves an accuracy of 98.9% for Weizmann, 99.63% for Berkeley MHAD, and 94.3% for MSR-Action3D. Moreover, a cross-dataset experiment is held to ensure the generality of the proposed technique, where the system is trained using Berkeley MHAD dataset and tested using MSR-Action3D, which achieved 88.76% accuracy. Moreover, a novel approach for handwriting actions recognition that depends on hands tracking is presented. This approach achieved an accuracy of 100%, and it can be considered an application for the proposed technique.

Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning.

We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions; then it obtains global representations of images by using sparse coding and a spatial pyramid; finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects—15 normal subjects, 15 AMD patients, and 15 DME patients; and clinical SD-OCT dataset, consisting of 678 OCT retina scans acquired from clinics in Beijing—168, 297, and 213 OCT images for AMD, DME, and normal retinas, respectively. For the former dataset, our classifier correctly identifies 100%, 100%, and 93.33% of the volumes with DME, AMD, and normal subjects, respectively, and thus performs much better than the conventional method; for the latter dataset, our classifier leads to a correct classification rate of 99.67%, 99.67%, and 100.00% for DME, AMD, and normal images, respectively.

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification.

Recognising lepidopteran images based on locality-constrained linear coding and SVM

In this paper, a novel lepidopteran insect images recognition method is proposed. The captured insect images are first preprocessed to separate the foreground from the complicated background. Then, two wings are cut out from the insect, calibrated and segmented into a number of superpixels. The values of l, a, b, x, y of each superpixel are calculated as feature descriptors. Those descriptors are encoded with locality-constrained linear coding and then pooled into a feature vector. Finally, the multi-class linear support vector machine is used for classification. The approach is testified in a data-set including 576 images of insect specimens from 10 different lepidopteran species and the recognition accuracy is over 99% in average. The experimental results suggested that the proposed method performs well in lepidopteran insect species recognition.

Automated Classification of Liver Disorders using Ultrasound Images

This paper presents a novel approach for detection of Fatty liver disease (FLD) and Heterogeneous liver using textural analysis of liver ultrasound images. The proposed system is able to automatically assign a representative region of interest (ROI) in a liver ultrasound which is subsequently used for diagnosis. This ROI is analyzed using Wavelet Packet Transform (WPT) and a number of statistical features are obtained. A multi-class linear support vector machine (SVM) is then used for classification. The proposed system gives an overall accuracy of ~95% which clearly illustrates the efficacy of the system.