scholarly journals Development of the "smart library" application using the Intel Distribution of OpenVINO toolkit

2019 ◽  
Author(s):  
Евгений Васильев ◽  
Evgeniy Vasil'ev ◽  
Валентина Кустикова ◽  
Valentina Kustikova ◽  
Иван Вихрев ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Source Code ◽  
Face Features ◽  
Machine Learning Model ◽  
Library Users ◽  
The Face

We represent a case study of using deep learning and computer vision library - the Intel Distribution of OpenVINO toolkit. We develop the automated “smart library” using DL and computer vision methods implemented in OpenVINO toolkit. The application involves the registration of the reader (adding information and photos of the new user); updating the machine learning model that describes the face features of the library users; authorization of the reader through face recognition; receiving and returning books by comparing the cover image with the database of flat images available in the library of books. The source code of the application is free available on GitHub: https://github.com/itlab-vision/openvino-smart-library. The developed application is planned to be published as a sample of the OpenVINO toolkit.

scholarly journals Real Time Face Recognition in Group Images using LBPH

2019 ◽  
Vol 8 (2) ◽  
pp. 1362-1367
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Face Recognition ◽  
Real Time ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Testing Dataset ◽  
Negative Images ◽  
The Face ◽  
Haar Cascade

Face recognition is a beneficial work in computer vision based applications. The goal of the proposed system is to provide complete face recognitions system capable of working a group of images. The faces are detected and verified the identity of an individual using a machine learning algorithm. The haar cascade detects the face from a group of images for training and testing dataset. The dataset contained positive and negative images for training and testing. The LBPH algorithm recognizes the faces from input images. The proposed system detects and recognizes faces with 98% accuracy

scholarly journals Face with Mask Detection and Recognition for Smart Attendance System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1586-1591
Author(s):  
Shweta Panjabrao Dhawale
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Face Mask ◽  
Learning Framework ◽  
The Face ◽  
Face Detection And Recognition ◽  
Python Programming ◽  
Detection And Recognition

In this paper we will see the face mask detection and recognition for smart attendance system. In current pandemic situation our proposed system is very useful. We have used here face algorithm technique, python programming and to capture the images open cv is used., open cv2 now comes with a very new face recognizer class for the face recognition and popular computer vision liberaay started by intel in 1999. Open cv released under BSD licence that’s why used in the academic projects. We have used the concept of deep learning framework for the detection of faces. our aim is to present the study of previous attempts at face detection and recognition for smart attendance system by using deep learning .these is rapidly growing technology with its application in various aspects.

Reviews

2020 ◽  
Vol 39 (6) ◽  
pp. 438-439
Author(s):  
Andreas Rüger ◽  
John Brittan ◽  
Robert Avakian
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Object Detection ◽  
Image Classification ◽  
Short Introduction ◽  
Seismic Processing ◽  
Oxford University ◽  
Oxford University Press

Deep learning for computer vision: Image classification, object detection, and face recognition in Python, by Jason Brownlee, 2020, Machine Learning Mastery, 563 p., US$0 (eBook). Illustrated Seismic Processing: Volume 1: Imaging, by Stephen J. Hill and Andreas Rüger, ISBN 978-1-560-80361-4, 2019, Society of Exploration Geophysicists, 330 p., US$39 (members), US$72 (nonmembers). Geology: A Very Short Introduction, by Jan Zalasiewicz, ISBN 978-0-198-80445-1, 2018, Oxford University Press, 168 p., US$11.95 (print).

scholarly journals Digital Attendance System using Face Recognition Technique

2021 ◽  
Vol 9 (12) ◽  
pp. 2273-2277
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
High Resolution ◽  
Data Science ◽  
Artificial Intelligent ◽  
Face Features ◽  
The Face ◽  
Recognition Technique

Abstract: In this era of digitalization, everything is interlinked and are online. Maximum of things are using ML (Machine Learning), AI (Artificial Intelligent), IoT, Data Science etc. Making use of this, an automated attendance system can be built. So, this project is proposing “Digital Attendance System” using “Face Recognition Technique”. Entering and keeping information in database and using algorithm to extract the face features, this way face recognition technique is achieved. And this technique is used to compare the captured image of source with that of database, resulting in Digital Attendance System which can be used to mark the attendance and so the motive is achieved. Keywords: Attendance system, Face Recognition Technique, dlib library, High resolution camera.

scholarly journals Generating Images of Face Poses for Pose Varying Face Recognition

2020 ◽  
Vol 9 (2) ◽  
pp. 351-356
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Representation Learning ◽  
Image Synthesis ◽  
Face Image ◽  
Learning Systems ◽  
Face Expression ◽  
Face Images ◽  
The Face

Deep learning has attracted several researchers in the field of computer vision due to its ability to perform face and object recognition tasks with high accuracy than the traditional shallow learning systems. The convolutional layers present in the deep learning systems help to successfully capture the distinctive features of the face. For biometric authentication, face recognition (FR) has been preferred due to its passive nature. Processing face images are accompanied by a series of complexities, like variation of pose, light, face expression, and make up. Although all aspects are important, the one that impacts the most face-related computer vision applications is pose. In face recognition, it has been long desired to have a method capable of bringing faces to the same pose, usually a frontal view, in order to ease recognition. Synthesizing different views of a face is still a great challenge, mostly because in nonfrontal face images there are loss of information when one side of the face occludes the other. Most solutions for FR fail to perform well in cases involving extreme pose variations as in such scenarios, the convolutional layers of the deep models are unable to find discriminative parts of the face for extracting information. Most of the architectures proposed earlier deal with the scenarios where the face images used for training as well as testing the deep learning models are frontal and nearfrontal. On the contrary, here a limited number of face images at different poses is used to train the model, where a number of separate generator models learn to map a single face image at any arbitrary pose to specific poses and the discriminator performs the task of face recognition along with discriminating a synthetic face from a realworld sample. To this end, this paper proposes a representation learning by rotating the face. Here an encoderdecoder structure of the generator enables to learn a representation that is both generative and discriminative, which can be used for face image synthesis and pose-invariant face recognition. This representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator.

scholarly journals Face recognition based on full convolutional neural network based on transfer learning model

2021 ◽  
pp. 28-28
Author(s):  
Zhongkui Fan ◽  
Ye-Peng Guan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Test Accuracy ◽  
Great Success ◽  
Data Set ◽  
Face Features ◽  
The Face

Deep learning has achieved a great success in face recognition (FR), however, little work has been done to apply deep learning for face photo-sketch recognition. This paper proposes an adaptive scale local binary pattern extraction method for optical face features. The extracted features are classified by Gaussian process. The most authoritative optical face test set LFW is used to train the trained model. Test, the test accuracy is 98.7%. Finally, the face features extracted by this method and the face features extracted from the convolutional neural network method are adapted to sketch faces through transfer learning, and the results of the adaptation are compared and analyzed. Finally, the paper tested the open-source sketch face data set CUHK Face Sketch database(CUFS) using the multimedia experiment of the Chinese University of Hong Kong. The test result was 97.4%. The result was compared with the test results of traditional sketch face recognition methods. It was found that the method recognized High efficiency, it is worth promoting.

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 Implementation of conventional communication system in deep learning

2017 ◽  
Vol 7 (1.1) ◽  
pp. 696
Author(s):  
Satyanarayana P ◽  
Charishma Devi. V ◽  
Sowjanya P ◽  
Satish Babu ◽  
N Syam Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Communication Network ◽  
Deep Learning ◽  
Communication System ◽  
Communication Systems ◽  
Physical Layer ◽  
Learning Capacity ◽  
Constrained Learning ◽  
Complex Channel

Machine learning (ML) has been broadly connected to the upper layers of communication systems for different purposes, for example, arrangement of cognitive radio and communication network. Nevertheless, its application to the physical layer is hindered by complex channel conditions and constrained learning capacity of regular ML algorithms. Deep learning (DL) has been as of late connected for some fields, for example, computer vision and normal dialect preparing, given its expressive limit and advantageous enhancement ability. This paper describes about a novel use of DL for the physical layer. By deciphering a communication system as an auto encoder, we build up an essential better approach to consider communication system outline as a conclusion to-end reproduction undertaking that tries to together enhance transmitter and receiver in a solitary procedure. This DL based technique demonstrates promising execution change than traditional communication system.  

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