scholarly journals A novel facial image recognition method based on perceptual hash using quintet triple binary pattern

2020 ◽  
Vol 79 (39-40) ◽  
pp. 29573-29593
Author(s):  
Turker Tuncer ◽  
Sengul Dogan ◽  
Moloud Abdar ◽  
Paweł Pławiak
Keyword(s):  
Face Recognition ◽  
Classification Accuracy ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Recognition Method ◽  
K Nearest Neighbors ◽  
Good Classification ◽  
Perceptual Hash ◽  
Face Datasets ◽  
Classification Capability

Abstract Image classification (categorization) can be considered as one of the most breathtaking domains of contemporary research. Indeed, people cannot hide their faces and related lineaments since it is highly needed for daily communications. Therefore, face recognition is extensively used in biometric applications for security and personnel attendance control. In this study, a novel face recognition method based on perceptual hash is presented. The proposed perceptual hash is utilized for preprocessing and feature extraction phases. Discrete Wavelet Transform (DWT) and a novel graph based binary pattern, called quintet triple binary pattern (QTBP), are used. Meanwhile, the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms are employed for classification task. The proposed face recognition method is tested on five well-known face datasets: AT&T, Face94, CIE, AR and LFW. Our proposed method achieved 100.0% classification accuracy for the AT&T, Face94 and CIE datasets, 99.4% for AR dataset and 97.1% classification accuracy for the LFW dataset. The time cost of the proposed method is O(nlogn). The obtained results and comparisons distinctly indicate that our proposed has a very good classification capability with short execution time.

scholarly journals Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 33 ◽  
Author(s):  
Firgan Feradov ◽  
Iosif Mporas ◽  
Todor Ganchev
Keyword(s):  
Visual Stimuli ◽  
Decomposition Methods ◽  
Automated Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Eeg Activity ◽  
Rbf Kernel ◽  
Eeg Features

There is a strong correlation between the like/dislike responses to audio–visual stimuli and the emotional arousal and valence reactions of a person. In the present work, our attention is focused on the automated detection of dislike responses based on EEG activity when music videos are used as audio–visual stimuli. Specifically, we investigate the discriminative capacity of the Logarithmic Energy (LogE), Linear Frequency Cepstral Coefficients (LFCC), Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT)-based EEG features, computed with and without segmentation of the EEG signal, on the dislike detection task. We carried out a comparative evaluation with eighteen modifications of the above-mentioned EEG features that cover different frequency bands and use different energy decomposition methods and spectral resolutions. For that purpose, we made use of Naïve Bayes classifier (NB), Classification and regression trees (CART), k-Nearest Neighbors (kNN) classifier, and support vector machines (SVM) classifier with a radial basis function (RBF) kernel trained with the Sequential Minimal Optimization (SMO) method. The experimental evaluation was performed on the well-known and widely used DEAP dataset. A classification accuracy of up to 98.6% was observed for the best performing combination of pre-processing, EEG features and classifier. These results support that the automated detection of like/dislike reactions based on EEG activity is feasible in a personalized setup. This opens opportunities for the incorporation of such functionality in entertainment, healthcare and security applications.

Classification of High Resolution Sar Imagery Using Local Indicators of Spatial Association

2013 ◽  
Vol 333-335 ◽  
pp. 1080-1084
Author(s):  
Zhang Fei ◽  
Ye Xi
Keyword(s):  
Support Vector Machines ◽  
High Resolution ◽  
Classification Accuracy ◽  
Spatial Association ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Good Classification ◽  
Sar Imagery

In this paper, we will propose a novel classification method of high-resolution SAR using local autocorrelation and Support Vector Machines (SVM) classifier. The commonly applied spatial autocorrelation indexes, called Moran's Index; Geary's Index, Getis's Index, will be used to depict the feature of the land-cover. Then, the SVM based on these indexes will be applied as the high-resolution SAR classifier. A Cosmo-SkyMed scene in ChengDu city, China is used for our experiment. It is shown that the method proposed can lead to good classification accuracy.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

scholarly journals Research on an Improved MB-LBP 3D Face Recognition Method

2021 ◽  
pp. 306-314
Author(s):  
Liangliang Shi ◽  
◽  
Xia Wang ◽  
Yongliang Shen
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Depth Image ◽  
Support Vector ◽  
3D Face Recognition ◽  
Recognition Method ◽  
Recognition Time ◽  
3D Face ◽  
Feature Information ◽  
Average Information

In order to improve the accuracy and speed of 3D face recognition, this paper proposes an improved MB-LBP 3D face recognition method. First, the MB-LBP algorithm is used to extract the features of 3D face depth image, then the average information entropy algorithm is used to extract the effective feature information of the image, and finallythe Support Vector Machine algorithm is used to identify the extracted effective information. The recognition rate on the Texas 3DFRD database is 96.88%, and the recognition time is 0.025s. The recognition rate in the self-made depth library is 96.36%, and the recognition time is 0.02s.It can be seen from the experimental results that the algorithm in this paper has better performance in terms of accuracy and speed.

scholarly journals Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Rana Alrawashdeh ◽  
Mohammad Al-Fawa'reh ◽  
Wail Mardini
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Wavelet Transforms ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Artificial Neural Network Ann ◽  
Manual Testing

Many approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue to rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML can be used to classify EEG signals employing feature extraction techniques. This work focuses on automated detection for epilepsy seizures using ML techniques. Various algorithms are investigated, such as  Bagging, Decision Tree (DT), Adaboost, Support vector machine (SVM), K-nearest neighbors(KNN), Artificial neural network(ANN), Naïve Bayes, and Random Forest (RF) to distinguish injected signals from normal ones with high accuracy. In this work, 54 Discrete wavelet transforms (DWTs) are used for feature extraction, and the similarity distance is applied to identify the most powerful features. The features are then selected to form the features matrix. The matrix is subsequently used to train ML. The proposed approach is evaluated through different metrics such as F-measure, precision, accuracy, and Recall. The experimental results show that the SVM and Bagging classifiers in some data set combinations, outperforming all other classifiers

scholarly journals Optimal filter bank representation in texture classification using support vector machines

2021 ◽  
Author(s):  
Samsher Singh Sidhu
Keyword(s):  
Support Vector Machines ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Texture Classification ◽  
Classification Method ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Translation Invariant ◽  
Vector Machines ◽  
Wavelet Frame Transform

Texture analysis has been a field of study for over three decades in many fields including electrical engineering. Today, texture analysis plays a crucial role in many tasks ranging from remote sensing to medical imaging. Researchers in this field have dealt with many different approaches, all trying to achieve the goal of high classification accuracy. The main difficulty of texture analysis was the lack of ability of the tools to characterize adequately different scales of the textures effectively. The development in multi-resolution analysis such as Gabor and Wavelet Transform help to overcome this difficulty. This thesis describes the texture classification algorithm that uses the combination of statistical features and co-occurrence features of the Discrete Wavelet Transformed images. The classification accuracy is increased by using translation-invariant features generated from the Discrete Wavelet Frame Transform. The results are further improved by focussing on the transformed images used for feature extraction by using filters which essentially extract those areas of the image that discriminate themselves from other image classes. In effect, by reducing the spatial characteristics of images that contribute to the features, the texture classification method still has the ability to preserve the classification accuracy. Support Vector Machines has proved excellent performance in the area of pattern recognition problems. We have applied SVMs with the texture classification method described above and, when compared to traditional classifiers, SVM has produced more accurate classification results on the Brodatz texture album.

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