scholarly journals Software Implementation of the Algorithm for Recognizing Protective Elements on The Face

2021 ◽  
Vol 6 (2) ◽  
pp. 155-160
Author(s):  
Mykola Voloshyn ◽  
◽  
Yevhenii Vavruk
Keyword(s):  
Neural Network ◽  
Fixed Number ◽  
Automated System ◽  
Raspberry Pi ◽  
Shopping Malls ◽  
The Neural Network ◽  
Python Scripting ◽  
The Face ◽  
Graphic File ◽  
Scripting Language

The quarantine restrictions introduced during COVID-19 are necessary to minimize the spread of coronavirus disease. These measures include a fixed number of people in the room, social distance, wearing protective equipment. These restrictions are achieved by the work of technological control workers and the police. However, people are not ideal creatures, quite often the human factor makes its adjustments. That is why in this work we have developed software for determining the protective elements on the face in real time using the Python scripting language, the open software libraries OpenCV v4.5.4, TensorFlow v2.6.0, Keras v2.6.0 and MobileNetV2 using the camera. The training program uses a prepared set of photos from KAGGLE — with a mask and without a mask. This set has been expanded by the authors to include different types of masks and their location. Using TensorFlow, Keras, MobileNetV2, a model is created to study the neural network by analyzing images. The generated neural network uses a model to determine the masks. You can preview the learning result of the network — it is presented as a graphic file. A program that uses the connected camera is then launched and the user can test the operation. This model can be easily deployed on embedded systems such as Raspberry Pi, Google Coral, and become a hardware and software automated system that can be used in crowded places — airports, shopping malls, stadiums, government agencies and more.

scholarly journals A Neural Network Decomposition Algorithm for Mapping on Crossbar-Based Computing Systems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1526 ◽  
Author(s):  
Choongmin Kim ◽  
Jacob A. Abraham ◽  
Woochul Kang ◽  
Jaeyong Chung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Predictive Performance ◽  
Fixed Number ◽  
Decomposition Algorithm ◽  
Computing Systems ◽  
Network Decomposition ◽  
Von Neumann ◽  
Analog Computing ◽  
The Neural Network

Crossbar-based neuromorphic computing to accelerate neural networks is a popular alternative to conventional von Neumann computing systems. It is also referred as processing-in-memory and in-situ analog computing. The crossbars have a fixed number of synapses per neuron and it is necessary to decompose neurons to map networks onto the crossbars. This paper proposes the k-spare decomposition algorithm that can trade off the predictive performance against the neuron usage during the mapping. The proposed algorithm performs a two-level hierarchical decomposition. In the first global decomposition, it decomposes the neural network such that each crossbar has k spare neurons. These neurons are used to improve the accuracy of the partially mapped network in the subsequent local decomposition. Our experimental results using modern convolutional neural networks show that the proposed method can improve the accuracy substantially within about 10% extra neurons.

Adaptive GLOH with PSO-trained NN for the recognition of plastic surgery faces and their types

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Archana Harsing Sable ◽  
Sanjay N. Talbar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Plastic Surgery ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Traditional Learning ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Orientation Histogram ◽  
The Face

Abstract Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.

scholarly journals Development of an algorithm for object access system automation based on face recognition using a neural network

Connectivity ◽  
2020 ◽  
Vol 145 (3) ◽  
Author(s):  
V. S. Orlenko ◽  
◽  
I. I. Kolosinsʹkyy
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Neural Model ◽  
Large Datasets ◽  
Second Step ◽  
Test Set ◽  
Access System ◽  
The Neural Network ◽  
The Face ◽  
Face Search

The article deals with the technical side of face recognition — the neural network. The advantages of the neural network for identification of the person are substantiated, the stages of comparison of two images are considered. The first step is defined as the face search in the photo. Using several tests, the best neural network was identified, which allowed to effectively obtain a normalized image of a person’s face. The second step is to find the features of the person, for which the comparative analysis is performed. It was this stage that became the main point in this article — 16 sets of tests were carried out, each test set has 12 tests inside. Two large datasets were used for the study to evaluate the effectiveness of the algorithms not only in ideal circumstances but also in the field. The results of the study allowed us to determine the best method and neural model for finding a face and dividing it into parts. It is determined which part of the face the algorithm recognizes best — it will allow making adjustments to the location of the camera.

Development of a Monitoring System for Near Burner Slag Formation

2002 ◽  
Author(s):  
C. K. Tan ◽  
S. J. Wilcox ◽  
J. Ward
Keyword(s):  
Neural Network ◽  
Refractory Material ◽  
Coal Combustion ◽  
Large Range ◽  
Slag Formation ◽  
Ir Radiation ◽  
The Neural Network ◽  
The Face ◽  
Series Of Experiments ◽  
Self Organising Map

A series of experiments on two different coals at a range of burner conditions have been conducted to investigate the behaviour of pf coal combustion on a 150kW pulverised fuel (pf) coal burner with a simulated eyebrow (a growth of slag in the near burner region). The simulation of a burner eyebrow was achieved by inserting an annulus of refractory material immediately in front of the face of the original burner quarl. Results obtained from monitoring the infrared (IR) radiation and sound emitted by the flame were processed into a number of features which were then used to train and test a self organising map neural network. Results obtained from the neural network demonstrated a classification success, never lower than 99.3%, indicate that it is not only possible to detect the presence of an eyebrow by monitoring the flame, but it is also possible to give an indication as to its size, over a reasonably large range of conditions.

OF PREDICTING THE RISK OF DEVELOPMENT OF MYOPIA IN YOUNG PEOPLE ON THE BASIS OF THE USE OF RADIAL NEURAL NETWORKS

2020 ◽  
pp. 92-97
Author(s):  
Игорь Эдуардович Есауленко ◽  
Юлия Владимировна Татаркова ◽  
Татьяна Николаевна Петрова ◽  
Олег Валериевич Судаков ◽  
Александр Юрьевич Гончаров
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Young People ◽  
Automated System ◽  
Neural Network Optimization ◽  
The Neural Network ◽  
Medical Institutions ◽  
Network Technologies ◽  
Medical University ◽  
Method Of Correlation

В статье рассматривается один из подходов к анализу и управлению рисками развития патологии глаз и его придаточного аппарата у лиц молодого возраста, основанный на нейросетевых технологиях. В работе приводится одна из возможных классификаций рисков, а также выделены области прогнозирования рисков, в которых применение нейронных сетей представляется наиболее эффективным. Выделены преимущества и недостатки нейронных сетей для задач прогнозирования и классификации характера течения миопии. Показывается преимущество их применения для приведения категориальных признаков к представлению, с которыми эффективно способна работать нейронная сеть. На основе сравнительного анализа выбраны функции активации для каждой группы риска миопии и алгоритм оптимизации нейронной сети. Подробно описаны метод для отбора признаков на основе работы построенной модели и основанный на поведении модели метод корреляционного анализа, который позволит решить характерную для нейронных сетей проблему неопределенности. Анализ оценки вероятности совершения ошибок первого и второго рода позволил сделать вывод о высокой обобщающей способности предлагаемого подхода. На основе оптимизированной нейронной сети была разработана автоматизированная система прогнозирования риска развития миопии в молодежной среде. Установлено, что предложенный авторами подход к оценке риска миопии у студентов медицинского вуза показал высокую прогностическую способность и может служить основой для создания информационной системы экспресс-диагностики патологии глаз и его придаточного аппарата у лиц молодого возраста. Внедрение разработанной системы позволит медицинским учреждениям повысить оперативность и точность предварительной диагностики пациентов The article discusses one of the approaches to the analysis and risk management of eye pathology and the adnexa in young people, based on neural network technologies. The paper presents one of the possible classifications of risks, and also identifies areas of risk prediction in which the use of neural networks seems to be most effective. The advantages of the disadvantages of neural networks for predicting and classifying the nature of the course of myopia are highlighted. The advantage of their use is shown to bring categorical features to a representation with which a neural network is effectively able to work. Based on a comparative analysis, the activation functions for each myopia risk group and the neural network optimization algorithm were selected. The method for selecting features based on the work of the constructed model and the method of correlation analysis based on the model’s behavior, which will solve the problem of uncertainty characteristic of neural networks, are described in detail. An analysis of the assessment of the probability of making mistakes of the first and second kind made it possible to conclude that the proposed approach is highly generalizing. Based on the optimized neural network, an automated system for predicting the risk of myopia in the youth environment was developed. It was established that the approach proposed by the authors to assess the risk of myopia in students of a medical university has shown high prognostic ability and can serve as the basis for creating an information system for the rapid diagnosis of eye pathology and its adnexa in young people. Implementation of the developed system will allow medical institutions to increase the efficiency and accuracy of preliminary diagnosis of patients

Research of object recognition using neural network Inception-v3 model operating on Raspberry Pi B3+

2021 ◽  
Vol 15 (1) ◽  
pp. 13-22
Author(s):  
An Toan Nguyen ◽  
◽  
Ngoc Thien Nguyen ◽  
Thanh Truc Nguyen
Keyword(s):  
Neural Network ◽  
Operating System ◽  
Computer Vision ◽  
Image Classification ◽  
Input Image ◽  
Raspberry Pi ◽  
Practical Applications ◽  
The Neural Network ◽  
Audio Channel ◽  
Compact Size

Image Classification is the most important problem in the field of computer vision. It is very simple and has many practical applications, the image classifier is responsible for assigning a label to the input image from a fixed category group. This article has applied image classification to identify objects by giving the image of the object to be identified, then labeling the image and announcing the label name (object name) through the audio channel. The classification is based on the neural network Inception-v3 model that has been trained on Tensorflow and used Raspberian operating system running on the Raspberry Pi 3 B+ to create a device capable of recognizing objects which compact size and convenient to apply in many fields.

scholarly journals A Low-cost Artificial Neural Network Model for Raspberry Pi

2020 ◽  
Vol 10 (2) ◽  
pp. 5466-5469 ◽  
Author(s):  
S. N. Truong
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Embedded System ◽  
Real Time ◽  
Image Recognition ◽  
Low Cost ◽  
Recognition Rate ◽  
Raspberry Pi ◽  
The Neural Network ◽  
Binary Array

In this paper, a ternary neural network with complementary binary arrays is proposed for representing the signed synaptic weights. The proposed ternary neural network is deployed on a low-cost Raspberry Pi board embedded system for the application of speech and image recognition. In conventional neural networks, the signed synaptic weights of –1, 0, and 1 are represented by 8-bit integers. To reduce the amount of required memory for signed synaptic weights, the signed values were represented by a complementary binary array. For the binary inputs, the multiplication of two binary numbers is replaced by the bit-wise AND operation to speed up the performance of the neural network. Regarding image recognition, the MINST dataset was used for training and testing of the proposed neural network. The recognition rate was as high as 94%. The proposed ternary neural network was applied to real-time object recognition. The recognition rate for recognizing 10 simple objects captured from the camera was 89%. The proposed ternary neural network with the complementary binary array for representing the signed synaptic weights can reduce the required memory for storing the model’s parameters and internal parameters by 75%. The proposed ternary neural network is 4.2, 2.7, and 2.4 times faster than the conventional ternary neural network for MNIST image recognition, speech commands recognition, and real-time object recognition respectively.

scholarly journals Deep Neural Network for Better Face Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 2236-2239
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Detection ◽  
Deep Neural Network ◽  
Low Cost ◽  
Raspberry Pi ◽  
The Face ◽  
Frame Work ◽  
Haar Cascade ◽  
Cascade Method

This Paper represents the face detection using advanced method deep neural network which uses deep learning frame work. The old models used to detect the faces were like Haar-cascade method which detect the faces with good approaches but there is some uncertainty in the accuracy of the old models, so in this system we will use the latest deep neural network model which is embedded with latest open cv and by using the deep learning model frame work which is weighted with some other files. By using this model, we can achieve the better accuracy in face detection which can be used for further purposes like auto focus in cameras, counting number of people etc. This model detects the faces accurately and paves the way for better recognition systems which can be used in many face biometric applications. For this purpose, low-cost computer board Raspberry Pi and Camera Sensor will be used.

scholarly journals Modelo para detectar el uso correcto de mascarillas en tiempo real utilizando redes neuronales convolucionales

2021 ◽  
Vol 9 (17) ◽  
pp. 111-120
Author(s):  
Hugo Andrade Carrera ◽  
Soraya Sinche Maita ◽  
Pablo Hidalgo Lascano
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Face Mask ◽  
Training Process ◽  
Public Places ◽  
The Neural Network ◽  
Great Performance ◽  
The Family ◽  
The Face ◽  
Mandatory Use

Since Covid-19 appeared, the world has entered into a new stage, in which everybody is trying to mitigate the effects of the virus. The mandatory use of face masks in public places and when maintaining contact with people outside the family circle is one of mandatory measures that many countries have implemented, such as Ecuador, thus, the purpose of this article is to develop a convolutional neural network model using TensorFlow based on MobileNetV2, that allows to perform mask detection in real time video with the key feature of determining if the person is using the face mask properly or if it is not wearing a mask, in order to use the model with OpenCV and a pretrained neural network that detects faces. In addition, the performance metrics of the neural network are analyzed, including precision, accuracy, recall and the F1 score. All performance metrics consider the number of epochs for the training process, obtaining as a result a model that classifies between three groups: faces without face mask, faces wearing a face mask improperly and faces wearing a mask properly. with a great performance in all metrics; The results show values greater than 85% for precision, recall and F1 score, and accuracy values between 93% for 5 epochs and 95% for 25 epochs.

scholarly journals Identify Attractive and Unattractive Individuals Based on Geometric Features Using Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1007-1012
Author(s):  
Shakiba Ahmadimehr ◽  
Mohammad Karimi Moridani
Keyword(s):  
Neural Network ◽  
Facial Attractiveness ◽  
Lateral Position ◽  
Geometric Features ◽  
Principle Components Analysis ◽  
Facial Beauty ◽  
The Neural Network ◽  
The Face ◽  
Classification Evaluation ◽  
Facial Images

This paper aims to explore the essence of facial attractiveness from the viewpoint of geometric features toward the classification and identification of attractive and unattractive individuals. We present a simple but useful feature extraction for facial beauty classification. Evaluation of facial attractiveness was performed with different combinations of geometric facial features using the deep learning method. In this method, we focus on the geometry of a face and use actual faces for our analysis. The proposed method has been tested on, image database containing 60 images of men's faces (attractive or unattractive) ranging from 20-50 years old. The images are taken from both frontal and lateral position. In the next step, principle components analysis (PCA) was applied to feature a reduction of beauty, and finally, the neural network was used for judging whether the obtained analysis of various faces is attractive or not. The results show that one of the indexes in identifying facial attractiveness base of science, is the values of the geometric features in the face, changing facial parameters can change the face from unattractive to attractive and vice versa. The experimental results are based on 60 facial images, high accuracy of 88%, and Sensitivity of 92% is obtained for 2-level classification (attractive or not).

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