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A Low-Cost Raspberry Pi-based System for Facial Recognition

Cristian Miranda Orostegui, Alejandro Navarro Luna, Andrés Manjarrés García, Carlos Augusto Fajardo Ariza
2021 Ingeniería y Ciencia  
In this research, we present an implementation of an embedded facial recognition system on a low cost Raspberry Pi, which is based on the FaceNet architecture.  ...  One major challenge is to fit the high resource demands of deep learning in less powerful edge computing devices.  ...  Conclusions We implemented a facial recognition system based on a deep learning architecture GoogLeNet on a Raspberry Pi model 3B+.  ... 
doi:10.17230/ingciencia.17.34.4 fatcat:m42zustefbe2jfxi5ftbjul4ae

Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application [article]

Rateb Jabbar, Mohammed Shinoy, Mohamed Kharbeche, Khalifa Al-Khalifa, Moez Krichen, Kamel Barkaoui
2020 arXiv   pre-print
The proposed CNN based model can be used to build a real-time driver drowsiness detection system for embedded systems and Android devices with high accuracy and ease of use.  ...  Our previous work in this field involved using machine learning with multi-layer perceptron to detect the same.  ...  The system was able to detect facial landmarks from images captured on a mobile device and pass it to a CNN-based trained Deep Learning model to detect drowsy driving behavior.  ... 
arXiv:2002.03728v1 fatcat:l5g5zbwgh5bojh3bzh4ykuvyfe


Arju Bano, Dr. Akash Saxena, Gaurav Kumar Das
2021 Zenodo  
We studied the fundamentals of face detecting and eye recognition with Haar Feature-based Cascade Classifiers in this article.  ...  We suggest a new system to determine the standard of the driver. Centered on face monitoring and facial main point identification of fatigue.  ...  The suggested CNN dependent model can be applied to construct an embedded system including Android devices with high accuracy and ease of usage with a real-time driver drowsiness detection system.  ... 
doi:10.5281/zenodo.5060281 fatcat:xrim7naubrc4jlduacs6xwur5a

Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization

Hsiao-Chi Li, Zong-Yue Deng, Hsin-Han Chiang
2020 Sensors  
Despite considerable progress in face recognition technology in recent years, deep learning (DL) and convolutional neural networks (CNN) have revealed commendable recognition effects with the advent of  ...  In applications in the security industry, lightweight and efficient face recognition are two key points for facilitating the deployment of DL and CNN models directly in field devices, due to their limited  ...  For recognizing a face in a real-time face recognition system, the process consists of four stages with a captured facial image being loaded into the system, including face detection, embedding generation  ... 
doi:10.3390/s20216114 pmid:33121101 pmcid:PMC7662273 fatcat:cytmkefgsna57pfuwlg3i4zolu

Automatic Attendance System for University Student Using Face Recognition Based on Deep Learning

Tata Sutabri, Pamungkur Pamungkur, Ade Kurniawan, Raymond Erz Saragih
2019 International Journal of Machine Learning and Computing  
In the proposed system, Convolutional Neural Network (CNN) is used to detect faces in images, deep metric learning is used to produce facial embedding, and K-NN is used to classify student's faces.  ...  Thus, the university administration is alleviated in recording attendance data. Index Terms-Student attendance system, convolutional neural network, deep metric learning, K-nearest neighbor.  ...  By using Convolutional Neural Network to detect a face, Dlib's CNN or deep metric learning for facial embedding, and K-NN to classify faces, the system successfully recognises the face of a student who  ... 
doi:10.18178/ijmlc.2019.9.5.856 fatcat:6rnlcedwuzdyxfuxh6mbjq7mj4

Papers by Title

2021 2021 International Conference on Advanced Technologies for Communications (ATC)  
estimation from facial images using residual regression model C 1 A B C D E F H I L M O P R S T U W CARS: Dynamic Cyber-attack Reaction in SDN-based Networks with Q-learning Chest X-ray abnormalities localization  ...  Intrusion Detection on Programmable Data Plane -based Learning Algorithms for Open World Malware Classification Efficient Multipath Routing Scheme for MPTCP-enable Software-Defined Networks Fast and Accurate  ... 
doi:10.1109/atc52653.2021.9598173 fatcat:ke4fex2jyvdjdhmczc73s2awfa

Driver Drowsiness Detection Using Convolutional Neural Networks

Md Gouse Pasha
2021 International Journal for Research in Applied Science and Engineering Technology  
In this case drowsiness is detected using an automatic camera, where, based on the captured image, the neural network detects whether the driver is awake or tired.  ...  We analyse image segmentation methods, construct a model based on convolutional neural networks.  ...  by Deep Learning.  ... 
doi:10.22214/ijraset.2021.36706 fatcat:3ogaq7kytff63idltthv7va7b4

Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks

Bhargava Reddy, Ye-Hoon Kim, Sojung Yun, Chanwon Seo, Junik Jang
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
In this paper, a novel approach towards real-time drowsiness detection based on deep learning which can be implemented on a low cost embedded board and performs with a high accuracy is proposed.  ...  This detector should be deployable to an embedded device and perform at high accuracy.  ...  The results thus conclude that our optimized deep neural networks model can be used for driver's drowsiness detection on embedded devices with a high accuracy for safety with Advanced Driver Assistance  ... 
doi:10.1109/cvprw.2017.59 dblp:conf/cvpr/ReddyKYSJ17 fatcat:7gh3ocfcpnekdcdng57wiqzini

Table of contents

2020 IEEE Transactions on Network and Service Management  
Mario Di Mauro, Giovanni Galatro, and Antonio Liotta 2480 Lightweight Misbehavior Detection Management of Embedded IoT Devices in Medical Cyber Physical Systems . . . . . . . . . . . . . . . . . . . .  ...  Zakaria Abou El Houda, Lyes Khoukhi, and Abdelhakim Senhaji Hafid 2523 Privacy-Preserved Task Offloading in Mobile Blockchain With Deep Reinforcement Learning . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tnsm.2020.3038983 fatcat:aufteleutjejdnwn2eqaurc5re

Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques [article]

Rateb Jabbar, Khalifa Al-Khalifa, Mohamed Kharbeche, Wael Alhajyaseen, Mohsen Jafari, Shan Jiang
2018 arXiv   pre-print
Keywords: Driver Monitoring System; Drowsiness Detection; Deep Learning; Real-time Deep Neural Network; Android.  ...  This approach is based on a deep learning method that can be implemented on Android applications with high accuracy.  ...  An increased embedding and connecting of smart devices equipped with sensors and mobile operating systems like Android, which has the largest installed operating system in cars, was shown by surveys in  ... 
arXiv:1811.01627v1 fatcat:yd4rjxmmmrfbvm4henraj2scyy

Table of Contents

2021 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS)  
............................... 266 SALAM Ransomware Behavior Analysis Challenges and Decryption ......................... 273 A Survey on Learning-Based Intrusion Detection Systems for IoT Networks ..  ...  approach based on pigeon inspired optimizer ........................................................................................................... 231 Robotics and Embedded Systems A Proposed Approach  ... 
doi:10.1109/icicis52592.2021.9694157 fatcat:qnwadgegdfbbrds3tvrz4wpr4q

Intruder detection and recognition using different image processing techniques for a proactive surveillance

Nelson C. Rodelas, Melvin A. Ballera
2021 Indonesian Journal of Electrical Engineering and Computer Science  
This device was programmed based on the different recognition algorithms and a criteria evaluation framework that could recognize intruders and burglars and the design used was developmental research to  ...  The criteria evaluation was used to identify the best face recognition algorithm and was tested in a real-world situation and captured a series of images camera and processed by proactive face detection  ...  We also thank the panelists who are always willing to help in the improvement of this paper.  ... 
doi:10.11591/ijeecs.v24.i2.pp843-852 fatcat:sh2xcbp3ufao5j7yjkum5m6o3y

Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model

Yang He, Shah Nazir, Baisheng Nie, Sulaiman Khan, Jianhui Zhang
2020 Complexity  
This model results with an accuracy rate of 93.87% for the LSTM-based model much better than 85.88% for the back-propagation-based deep model.  ...  To provide an optimal solution to these generic problems, the proposed research work aims to implement a deep learning-based pervasive computing architecture to address these problems.  ...  In our case, we have used the deep learning-based model to defend the unauthorized access and malicious network traffic. Deep Learning-Based Intrusion Detection System.  ... 
doi:10.1155/2020/4579495 fatcat:ivm5pgeedrbtznofig6mcnbima

SUST-DDD: A Real-Drive Dataset for Driver Drowsiness Detection

Esra Kavalci Yilmaz, M. Ali Akcayol
2022 Zenodo  
In this study, a new dataset for drowsiness has been created and some kind of deep learning methods such as AlexNet, LSTM, VGG16, VGG19, VGGFaceNet and hybrid deep networks have been applied on this dataset  ...  Three techniques are used to detect drowsiness: (1) based on vehicle parameters, (2) based on physiological parameters and (3) based on behavioral parameters.  ...  ACKNOWLEDGMENT The experiments reported in this study were carried out with the workstation of Sivas University of Science and Technology.  ... 
doi:10.5281/zenodo.6519933 fatcat:th3dws3rdrbhdd7ol33wnogez4

A Hybrid Deep Learning Based Visual System for In-Vehicle Safety

Rajkumar Joghee Bhojan, D. Ramyachitra, Subramanian Ganesan, Ragavi Rajkumar
2019 European Journal of Engineering Research and Science  
In this research paper, we propose a hybrid deep learning based visual system for providing feedback to the driver in a non-intrusive manner.  ...  In the automotive industry, researchers, AI experts, and developers are actively pushing deep learning based approaches for In-vehicle safety.  ...  A Hybrid Deep Learning Based Visual System for In-Vehicle Safety Rajkumar Joghee Bhojan, D.  ... 
doi:10.24018/ejers.2019.4.4.1185 fatcat:5fbsvyia4rbvvjrnq4d2nnneie
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