scholarly journals A NOVEL METHOD FOR PROTECTIVE FACE MASK DETECTION USING CONVOLUTIONAL NEURAL NETWORKS AND IMAGE HISTOGRAMS

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 177-182
Author(s):  
E. Ryumina ◽  
D. Ryumin ◽  
D. Ivanko ◽  
A. Karpov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Hybrid Method ◽  
Face Mask ◽  
Corpus Analysis ◽  
Visual Features ◽  
Pixel Intensity ◽  
Novel Method ◽  
Protective Mask ◽  
Detection And Recognition

Abstract. This paper proposes a new hybrid method for automatic detection and recognition of the presence/absence of a protective mask on human's face. It combines visual features extracted using Convolutional Neural Network (CNN) with image histograms that convey information about pixel intensity. Several pre-trained models for building feature extraction systems using a CNN and several types of image histograms are considered in this paper. We test our approach on the Medical Mask Dataset and perform cross-corpus analysis on two other databases named Masked Faces (MAFA) and Real-World Masked Face Dataset (RMFD). We demonstrate that the proposed hybrid method increases the Unweighted Average Recalls (UARs) of recognition of the presence/absence of a protective mask on human's face in comparison with traditional CNNs on the MAFA and RMFD databases by 0.96% and 1.32%, respectively. The proposed method can be generalized and used for other tasks of biometry, computer vision, machine learning and automatic face recognition.

Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services

2012 ◽  
pp. 106-121
Author(s):  
Kensuke Naoe ◽  
Hideyasu Sasaki ◽  
Yoshiyasu Takefuji
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Digital Watermarking ◽  
Visual Features ◽  
Learning Technology ◽  
Key Generation ◽  
Visual Objects ◽  
Training Approach ◽  
Intelligent Information ◽  
Mobile Collaboration

The Service-Oriented Architecture (SOA) demands supportive technologies and new requirements for mobile collaboration across multiple platforms. One of its representative solutions is intelligent information security of enterprise resources for collaboration systems and services. Digital watermarking became a key technology for protecting copyrights. In this article, the authors propose a method of key generation scheme for static visual digital watermarking by using machine learning technology, neural network as its exemplary approach for machine learning method. The proposed method is to provide intelligent mobile collaboration with secure data transactions using machine learning approaches, herein neural network approach as an exemplary technology. First, the proposed method of key generation is to extract certain type of bit patterns in the forms of visual features out of visual objects or data as training data set for machine learning of digital watermark. Second, the proposed method of watermark extraction is processed by presenting visual features of the target visual image into extraction key or herein is a classifier generated in advance by the training approach of machine learning technology. Third, the training approach is to generate the extraction key, which is conditioned to generate watermark signal patterns, only if proper visual features are presented to the classifier. In the proposed method, this classifier which is generated by the machine learning process is used as watermark extraction key. The proposed method is to contribute to secure visual information hiding without losing any detailed data of visual objects or any additional resources of hiding visual objects as molds to embed hidden visual objects. In the experiments, they have shown that our proposed method is robust to high pass filtering and JPEG compression. The proposed method is limited in its applications on the positions of the feature sub-blocks, especially on geometric attacks like shrinking or rotation of the image.

scholarly journals A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6491
Author(s):  
Le Zhang ◽  
Jeyan Thiyagalingam ◽  
Anke Xue ◽  
Shuwen Xu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Classification Accuracy ◽  
Signal Amplitude ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Amplitude Change ◽  
Novel Method

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions.

scholarly journals IMPLEMENTASI FACE MASK DETECTION UNTUK MENGHAMBAT LAJU PENYEBARAN COVID-19 BERBASIS MACHINE LEARNING

2021 ◽  
Vol 5 (2) ◽  
pp. 413
Author(s):  
Reza Marwansyah ◽  
Astriana Mulyani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Face Mask ◽  
Computing Resource

Terus meningkatnya kasus Coronavirus Disease-2019 (COVID-19) di Indonesia disebabkan karena masyarakat selama ini tidak disiplin dalam menerapkan protokol kesehatan khususnya dalam penggunaan masker. Dalam hal ini, upaya pemerintah Indonesia untuk menghambat laju penyebaran COVID-19 yaitu dengan melakukan razia masker. Kurangnya personel dalam menjalankan operasi razia masker menjadi kendala yang harus dihadapi. Agar penerapan disiplin dalam menggunakan masker berjalan dengan baik, maka dibuatlah Face Mask Detection. Face Mask Detection ini berbasis Machine Learning dengan algoritma Convolutional Neural Network (CNN) berarsitektur MobileNetV2. MobileNetV2 mampu mengefisiensi penggunaan computing resource yang tinggi dibandingkan dengan arsitektur CNN lainnya. Hasil training tersebut menghasilkan model dengan nilai accuracy 93%. Hasil dari penelitian ini adalah sistem yang mampu mendeteksi apakah orang tersebut menggunakan masker atau tidak.

scholarly journals Improved Crack Detection and Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Keqin Chen ◽  
Amit Yadav ◽  
Asif Khan ◽  
Yixin Meng ◽  
Kun Zhu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Crack Detection ◽  
Recognition Rate ◽  
Machine Learning Methods ◽  
Long Time ◽  
Detection And Recognition

Concrete cracks are very serious and potentially dangerous. There are three obvious limitations existing in the present machine learning methods: low recognition rate, low accuracy, and long time. Improved crack detection based on convolutional neural networks can automatically detect whether an image contains cracks and mark the location of the cracks, which can greatly improve the monitoring efficiency. Experimental results show that the Adam optimization algorithm and batch normalization (BN) algorithm can make the model converge faster and achieve the maximum accuracy of 99.71%.

scholarly journals Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks

2021 ◽  
Author(s):  
Ismail Alarab ◽  
Simant Prakoonwit
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Model Uncertainty ◽  
Uncertainty Estimation ◽  
Decision Boundary ◽  
Uncertainty Estimates ◽  
Data Points ◽  
Novel Method ◽  
Adversarial Attack ◽  
Critical Regions

AbstractWe propose a novel method to capture data points near decision boundary in neural network that are often referred to a specific type of uncertainty. In our approach, we sought to perform uncertainty estimation based on the idea of adversarial attack method. In this paper, uncertainty estimates are derived from the input perturbations, unlike previous studies that provide perturbations on the model's parameters as in Bayesian approach. We are able to produce uncertainty with couple of perturbations on the inputs. Interestingly, we apply the proposed method to datasets derived from blockchain. We compare the performance of model uncertainty with the most recent uncertainty methods. We show that the proposed method has revealed a significant outperformance over other methods and provided less risk to capture model uncertainty in machine learning.

Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services

2012 ◽  
pp. 631-646
Author(s):  
Kensuke Naoe ◽  
Hideyasu Sasaki ◽  
Yoshiyasu Takefuji
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Digital Watermarking ◽  
Visual Features ◽  
Learning Technology ◽  
Key Generation ◽  
Visual Objects ◽  
Training Approach ◽  
Intelligent Information ◽  
Mobile Collaboration

The Service-Oriented Architecture (SOA) demands supportive technologies and new requirements for mobile collaboration across multiple platforms. One of its representative solutions is intelligent information security of enterprise resources for collaboration systems and services. Digital watermarking became a key technology for protecting copyrights. In this article, the authors propose a method of key generation scheme for static visual digital watermarking by using machine learning technology, neural network as its exemplary approach for machine learning method. The proposed method is to provide intelligent mobile collaboration with secure data transactions using machine learning approaches, herein neural network approach as an exemplary technology. First, the proposed method of key generation is to extract certain type of bit patterns in the forms of visual features out of visual objects or data as training data set for machine learning of digital watermark. Second, the proposed method of watermark extraction is processed by presenting visual features of the target visual image into extraction key or herein is a classifier generated in advance by the training approach of machine learning technology. Third, the training approach is to generate the extraction key, which is conditioned to generate watermark signal patterns, only if proper visual features are presented to the classifier. In the proposed method, this classifier which is generated by the machine learning process is used as watermark extraction key. The proposed method is to contribute to secure visual information hiding without losing any detailed data of visual objects or any additional resources of hiding visual objects as molds to embed hidden visual objects. In the experiments, they have shown that our proposed method is robust to high pass filtering and JPEG compression. The proposed method is limited in its applications on the positions of the feature sub-blocks, especially on geometric attacks like shrinking or rotation of the image.

Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services

2010 ◽  
Vol 1 (1) ◽  
pp. 46-61
Author(s):  
Kensuke Naoe ◽  
Hideyasu Sasaki ◽  
Yoshiyasu Takefuji
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Digital Watermarking ◽  
Visual Features ◽  
Learning Technology ◽  
Key Generation ◽  
Data Set ◽  
Visual Objects ◽  
Training Approach ◽  
Mobile Collaboration

The Service-Oriented Architecture (SOA) demands supportive technologies and new requirements for mobile collaboration across multiple platforms. One of its representative solutions is intelligent information security of enterprise resources for collaboration systems and services. Digital watermarking became a key technology for protecting copyrights. In this article, the authors propose a method of key generation scheme for static visual digital watermarking by using machine learning technology, neural network as its exemplary approach for machine learning method. The proposed method is to provide intelligent mobile collaboration with secure data transactions using machine learning approaches, herein neural network approach as an exemplary technology. First, the proposed method of key generation is to extract certain type of bit patterns in the forms of visual features out of visual objects or data as training data set for machine learning of digital watermark. Second, the proposed method of watermark extraction is processed by presenting visual features of the target visual image into extraction key or herein is a classifier generated in advance by the training approach of machine learning technology. Third, the training approach is to generate the extraction key, which is conditioned to generate watermark signal patterns, only if proper visual features are presented to the classifier. In the proposed method, this classifier which is generated by the machine learning process is used as watermark extraction key. The proposed method is to contribute to secure visual information hiding without losing any detailed data of visual objects or any additional resources of hiding visual objects as molds to embed hidden visual objects. In the experiments, they have shown that our proposed method is robust to high pass filtering and JPEG compression. The proposed method is limited in its applications on the positions of the feature sub-blocks, especially on geometric attacks like shrinking or rotation of the image.

scholarly journals COVID-19: Face Mask Detector with Open CV and CNN Algorithm

2021 ◽  
Author(s):  
Tambe Pragati ◽  
Tanpure Akshada ◽  
Wakchaure Asmita ◽  
Zaware Prachi ◽  
S.A. Bhosale
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Making ◽  
Face Mask ◽  
Machine Learning Method ◽  
Learning Method ◽  
Infected Person ◽  
Public Places ◽  
The Public ◽  
Reduce Risk

Now a day, covering our faces with a mask has become a new normal habit in this pandemic, as face masks are effective in preventing the virus outbreak. Masks reduce risk from an infected person whether they have symptoms or not. In this paper, we propose a system that restrict the growth of COVID-19 by finding out peoples with mask and without mask. Where all the public places are monitored with CCTV cameras. A deep learning architecture is trained on a dataset which consists of images of people with wearing mask and without wearing is masks collected from various sources. By using image processing analysis and machine learning method we can find out face mask wearied or not. Face mask detection can be done using various methods. Mainly convolutional neural network and OpenCV method is used. The accuracy and decision making of CNN algorithm is higher than other algorithms.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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