scholarly journals Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study

2021 ◽  
Vol 11 (14) ◽  
pp. 6522
Author(s):  
Chiranjeevi Karri ◽  
Omar Cheikhrouhou ◽  
Ahmed Harbaoui ◽  
Atef Zaguia ◽  
Habib Hamam
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Security And Privacy ◽  
Cloud Robotics ◽  
Encryption Algorithms ◽  
And Performance ◽  
Set Up ◽  
Cloud Servers ◽  
Robotic Applications

Real-time robotic applications encounter the robot on board resources’ limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition task’s accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms.

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.

scholarly journals DIGITAL IMAGE PROCESSING AND RECOGNITION USING PYTHON

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
D. Sri Shreya
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Digital Image Processing ◽  
Digital Image ◽  
Gaussian Function ◽  
Numerical Data ◽  
Input Image ◽  
Image Blurring ◽  
Learning Concept

In this project, the primary aim will be the conversion of images into Grayscale in which conversion of pixels to array takes place and apply Blur effect using The Gaussian blur which is a type of image-blurring filter that uses a Gaussian function which also expresses the normal distribution in statistics for calculating the transformation to apply to each pixel in the image. The above two processesare applied to the input images. These two above mentioned processes can be achieved by utilizing the most relevant python libraries and functions, followed by conversion of the digital image to numerical data and then, applying the effects to the image to get back the image with applied effects in it. Face recognition refers to matching a face present in an input image from the training/pre-saved dataset and by applying Deep Learning Concept. This will be achieved by defining a function to read and convert images to data, apply the python function, and then, recreating the image with results.

scholarly journals Deep Learning based Image Processing for Cashier-less Self-Checkout Methodology

2020 ◽  
Vol 9 (1) ◽  
pp. 2291-2294
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Stereo Vision ◽  
Large Scale ◽  
Online Shopping ◽  
Pressure Sensors ◽  
Rfid Tags ◽  
Weight Measurement ◽  
The People

In recent years, shopping experiences are becoming more advanced. These include the attempts of market shelves as well as the currently booming online shopping. Online shopping has a better convenience but not yet accepted on a large scale by many people. Retail shops still retain greater response by the users and thus the retailers are moving towards an attempt of cashier-less shopping. A major problem of retail shops is that the people have crunch-time for shopping and cannot afford the waiting time at the checkout counters. Addressing this problem, we have developed a shopping style which saves time of checkout and also the time of maintaining social distancing queues. This research paper presents a stereo vision-based AI system which is useful to monitor the customers while shopping and also the items which are added or replaced in the virtual cart. The customers can directly walk out of the store after shopping and the final order cost of the shopping will be evaluated. This amount will be charged to the customer’s account. The system makes sure that there are no errors made during the evaluation and there are no charges for products which are not brought home. To achieve all this, the system uses image processing, object detection and face recognition algorithms that are widely practiced at present. The system also uses sensors like RFID tags and pressure sensors for weight measurement and detection of products on the shelves.

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Teacher digital competence: bringing education to the next level

2020 ◽  
Vol 11 (1) ◽  
pp. 22-26
Author(s):  
S.V. Tsymbal ◽  
Keyword(s):  
Deep Learning ◽  
Learning Experiences ◽  
Digital Competence ◽  
Digital Revolution ◽  
Access Information ◽  
Set Up ◽  
Insight Into ◽  
The Way

The digital revolution has transformed the way people access information, communicate and learn. It is teachers' responsibility to set up environments and opportunities for deep learning experiences that can uncover and boost learners’ capacities. Twentyfirst century competences can be seen as necessary to navigate contemporary and future life, shaped by technology that changes workplaces and lifestyles. This study explores the concept of digital competence and provide insight into the European Framework for the Digital Competence of Educators.

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.

Deep Learning-Based Image Processing for Whitecaps on the Ocean Surface

2020 ◽  
Vol 76 (2) ◽  
pp. I_163-I_168
Author(s):  
Yukun WANG ◽  
Yuji SUGIHARA ◽  
Xianting ZHAO ◽  
Haruki NAKASHIMA ◽  
Osama ELJAMAL
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Ocean Surface
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