Scientific and Anatomical Character Reading from the Face Using Deep Learning

2019 ◽  
Author(s):  
Srinivas Adapa ◽  
Vamsidhar Enireddy
Keyword(s):  
Deep Learning ◽  
Anatomical Character ◽  
The Face ◽  
Character Reading

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

scholarly journals Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3046
Author(s):  
Shervin Minaee ◽  
Mehdi Minaei ◽  
Amirali Abdolrashidi
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Visualization Technique ◽  
Convolutional Network ◽  
The Past ◽  
Multiple Datasets ◽  
The Face ◽  
Traditional Approaches

Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.

scholarly journals IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3495
Author(s):  
Shabir Hussain ◽  
Yang Yu ◽  
Muhammad Ayoub ◽  
Akmal Khan ◽  
Rukhshanda Rehman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Face Mask ◽  
World Health ◽  
Rapid Screening ◽  
Screening System ◽  
Source Of Infection ◽  
The Face ◽  
Health Organization ◽  
Vaccine Distribution

The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.

The Development of an Identification Photo Booth System based on a Deep Learning Automatic Image Capturing Method

2020 ◽  
Author(s):  
Yu-Xiang Zhao ◽  
Yi-Zeng Hsieh ◽  
Shih-Syun Lin
Keyword(s):  
Deep Learning ◽  
Experimental Results ◽  
Automatic Annotation ◽  
Learning Method ◽  
Facial Region ◽  
Facial Landmarks ◽  
Image Capturing ◽  
The Face ◽  
Facial Contours

With advances in technology, photo booths equipped with automatic capturing systems have gradually replaced the identification (ID) photo service provided by photography studios, thereby enabling consumers to save a considerable amount of time and money. Common automatic capturing systems employ text and voice instructions to guide users in capturing their ID photos; however, the capturing results may not conform to ID photo specifications. To address this issue, this study proposes an ID photo capturing algorithm that can automatically detect facial contours and adjust the size of captured images. The authors adopted a deep learning method (You Only Look Once) to detect the face and applied a semi-automatic annotation technique of facial landmarks to find the lip and chin regions from the facial region. In the experiments, subjects were seated at various distances and heights for testing the performance of the proposed algorithm. The experimental results show that the proposed algorithm can effectively and accurately capture ID photos that satisfy the required specifications.

scholarly journals Face Recognition Model Using Back Propagation

2018 ◽  
Vol 7 (3.34) ◽  
pp. 237
Author(s):  
R Aswini Priyanka ◽  
C Ashwitha ◽  
R Arun Chakravarthi ◽  
R Prakash
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Face Identification ◽  
Identification System ◽  
Scientific World ◽  
The Face ◽  
Deep Learning Neural Network

In scientific world, Face recognition becomes an important research topic. The face identification system is an application capable of verifying a human face from a live videos or digital images. One of the best methods is to compare the particular facial attributes of a person with the images and its database. It is widely used in biometrics and security systems. Back in old days, face identification was a challenging concept. Because of the variations in viewpoint and facial expression, the deep learning neural network came into the technology stack it’s been very easy to detect and recognize the faces. The efficiency has increased dramatically. In this paper, ORL database is about the ten images of forty people helps to evaluate our methodology. We use the concept of Back Propagation Neural Network (BPNN) in deep learning model is to recognize the faces and increase the efficiency of the model compared to previously existing face recognition models.   

scholarly journals Predicting the Landscape of Recombination Using Deep Learning

2020 ◽  
Vol 37 (6) ◽  
pp. 1790-1808 ◽  
Author(s):  
Jeffrey R Adrion ◽  
Jared G Galloway ◽  
Andrew D Kern
Keyword(s):  
Deep Learning ◽  
Evolutionary History ◽  
Model Misspecification ◽  
Natural Populations ◽  
High Accuracy ◽  
Demographic Model ◽  
Recombination Rates ◽  
Genome Wide ◽  
A Genome ◽  
The Face

Abstract Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage influence everything from genetic mapping to understanding evolutionary history. Here, we describe recombination landscape estimation using recurrent neural networks (ReLERNN), a deep learning method for estimating a genome-wide recombination map that is accurate even with small numbers of pooled or individually sequenced genomes. Rather than use summaries of linkage disequilibrium as its input, ReLERNN takes columns from a genotype alignment, which are then modeled as a sequence across the genome using a recurrent neural network. We demonstrate that ReLERNN improves accuracy and reduces bias relative to existing methods and maintains high accuracy in the face of demographic model misspecification, missing genotype calls, and genome inaccessibility. We apply ReLERNN to natural populations of African Drosophila melanogaster and show that genome-wide recombination landscapes, although largely correlated among populations, exhibit important population-specific differences. Lastly, we connect the inferred patterns of recombination with the frequencies of major inversions segregating in natural Drosophila populations.

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

Face Counting and Mask Detection using Deep Learning

2020 ◽  
Vol 11 (12) ◽  
pp. 709-714
Author(s):  
Janani Prabu ◽  
Sai Saranesh ◽  
Dr.S. Ajitha
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Strong Solution ◽  
Real World ◽  
Human Communication ◽  
Human Ability ◽  
Real World Applications ◽  
The Face

Face is one among the foremost important human's biometrics which is used frequently in every day human communication and due to some of its unique characteristics plays a major role in conveying identity and emotion. So far numerous methods have been proposed for face recognition, but it's still remained very challenging in real world applications and up to date; there is no technique which equals human ability to recognize faces despite many variations in appearance that the face can have in a scene and provides a strong solution to all situations.

scholarly journals Do androids dream of electric sorghum?: Predicting Phenotypes from Multi-Scale Genomic and Environmental Data using Neural Networks and Knowledge Graphs

2020 ◽  
Author(s):  
Ryan Bartelme ◽  
Michael Behrisch ◽  
Emily Jean Cain ◽  
Remco Chang ◽  
Ishita Debnath ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Scale Effects ◽  
Environmental Data ◽  
Phenotypic Data ◽  
Multi Scale ◽  
Data Schema ◽  
The Face ◽  
Disparate Data ◽  
Knowledge Graphs

The interplay between an organism's genes, its environment, and the expressed phenotype is dynamic. These interactions within ecosystems are shaped by non-linear multi-scale effects that are difficult to disentangle into discrete components. In the face of anthropogenic climate chance, it is critical to understand environmental and genotypic influences on plant phenotypes and phenophase transitions. However, it is difficult to integrate and interoperate between these datasets. Advances in the fields of ontologies, unsupervised learning, and genomics may overcome the disparate data schema. Here we present a framework to better link phenotypes, environments, and genotypes of plant species across ecosystem scales. This approach utilizing phenotypic data, knowledge graphing, and deep learning, serves as the groundwork for a new scientific sub-discipline: “Computational Ecogenomics”

scholarly journals Focused small-scale fisheries as complex systems using deep learning models

2021 ◽  
Vol 49 (2) ◽  
pp. 342-353
Author(s):  
Ricardo Cavieses-Núñez ◽  
Miguel A. Ojeda-Ruiz ◽  
Alfredo Flores-Irigollen ◽  
Elvia Marín-Monroy ◽  
Mirtha Lbañez-Lucero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Environmental Variables ◽  
Predictive Power ◽  
Small Scale ◽  
Learning Models ◽  
Small Scale Fisheries ◽  
Productive Activities ◽  
Lack Of Information ◽  
Management Measures ◽  
The Face

Small-scale fishing (SSF) is a relevant economic activity worldwide, so sustainable development will be essential to assure its contributions to food security, poverty alleviation, and healthy ecosystems. However, the wide diversity of fisheries, their complexity, and the lack of information limit the ability to propose/evaluate management measures and plans and their effects on communities and other productive activities. The state of Baja California Sur, Mexico, our study case, ranks as the third place in national fisheries production, possesses SSF fleets, has a wide variety of fisheries that share fishing areas, fishing seasons, and operating units. In this work, assuming SSF as a complex system were proposed deep learning models (DLM) to forecast the catch volumes, evaluate each input variable's importance, and find interactions. Environmental variables and catch fisheries were tested in the DLM to estimate their predictive power. Different DLM structures and parameters to find the optimal model was used. The variables that presented higher predictive power are the environmental variables with R = 0.90. Moreover, when used in combination with the catches from other areas, the performance of R = 0.95 is obtained. Using only the catches, the model has an R = 0.81. This model allows the use of variables that indirectly affect the system and demonstrates a useful tool to assess a complex system's state in the face of disturbances in its variables.

Sign in / Sign up

Export Citation Format

Share Document