scholarly journals An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance

2018 ◽  
Vol 8 (11) ◽  
pp. 2222 ◽  
Author(s):  
Chengbin Peng ◽  
Wei Bu ◽  
Jiangjian Xiao ◽  
Ka-chun Wong ◽  
Minmin Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Public Safety ◽  
Low Resolution ◽  
Large Area ◽  
Large Margin ◽  
The Face ◽  
Cascade Algorithms ◽  
Area Surveillance

Face detection for security cameras monitoring large and crowded areas is very important for public safety. However, it is much more difficult than traditional face detection tasks. One reason is, in large areas like squares, stations and stadiums, faces captured by cameras are usually at a low resolution and thus miss many facial details. In this paper, we improve popular cascade algorithms by proposing a novel multi-resolution framework that utilizes parallel convolutional neural network cascades for detecting faces in large scene. This framework utilizes the face and head-with-shoulder information together to deal with the large area surveillance images. Comparing with popular cascade algorithms, our method outperforms them by a large margin.

scholarly journals A New Convolutional Neural Network based on a Saprse Convolutional Layer for Animal Face Detection

2021 ◽  
Author(s):  
Islem Jarraya ◽  
Fatma BenSaid ◽  
Wael Ouarda ◽  
Umapada Pal ◽  
Adel Alimi
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Feature Selection Method ◽  
Automated Negotiation ◽  
The Face ◽  
Online Feature Selection ◽  
Fully Connected ◽  
Sparse Feature Selection

This paper focuses on the face detection problem of three popular animal cat-egories that need control such as horses, cats and dogs. To be precise, a new Convolutional Neural Network for Animal Face Detection (CNNAFD) is actu-ally investigated using processed filters based on gradient features and applied with a new way. A new convolutional layer is proposed through a sparse feature selection method known as Automated Negotiation-based Online Feature Selection (ANOFS). CNNAFD ends by stacked fully connected layers which represent a strong classifier. The fusion of CNNAFD and MobileNetV2 constructs the newnetwork CNNAFD-MobileNetV2 which improves the classification results and gives better detection decisions. Our work also introduces a new Tunisian Horse Detection Database (THDD). The proposed detector with the new CNNAFD-MobileNetV2 network achieved an average precision equal to 99.78%, 99% and 98.28% for cats, dogs and horses respectively.

Driver fatigue detection based on convolutional neural network and face alignment for edge computing device

2021 ◽  
pp. 095440702199948
Author(s):  
Xiaofeng Li ◽  
Jiahao Xia ◽  
Libo Cao ◽  
Guanjun Zhang ◽  
Xiexing Feng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Edge Computing ◽  
Face Alignment ◽  
Driver Fatigue ◽  
Computing Device ◽  
The Face ◽  
Yaw Angle ◽  
Fatigue Detection

Most current vision-based fatigue detection methods don’t have high-performance and robust face detector. They detect driver fatigue using single detection feature and cannot achieve real-time efficiency on edge computing devices. Aimed at solving these problems, this paper proposes a driver fatigue detection system based on convolutional neural network that can run in real-time on edge computing devices. The system firstly uses the proposed face detection network LittleFace to locate the face and classify the face into two states: small yaw angle state “normal” and large yaw angle state “distract.” Secondly, the speed-optimized SDM algorithm is conducted only in the face region of the “normal” state to deal with the problem that the face alignment accuracy decreases at large angle profile, and the “distract” state is used to detect driver distraction. Finally, feature parameters EAR, MAR and head pitch angle are calculated from the obtained landmarks and used to detect driver fatigue respectively. Comprehensive experiments are conducted to evaluate the proposed system and the results show its practicality and superiority. Our face detection network LittleFace can achieve 88.53% mAP on AFLW test set at 58 FPS on the edge computing device Nvidia Jetson Nano. Evaluation results on YawDD, 300 W, and DriverEyes show the average detection accuracy of the proposed system can reach 89.55%.

scholarly journals A New Convolutional Neural Network based on a Saprse Convolutional Layer for Animal Face Detection

2021 ◽  
Author(s):  
Islem Jarraya ◽  
Fatma BenSaid ◽  
Wael Ouarda ◽  
Umapada Pal ◽  
Adel Alimi
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Feature Selection Method ◽  
Automated Negotiation ◽  
The Face ◽  
Online Feature Selection ◽  
Fully Connected ◽  
Sparse Feature Selection

This paper focuses on the face detection problem of three popular animal cat-egories that need control such as horses, cats and dogs. To be precise, a new Convolutional Neural Network for Animal Face Detection (CNNAFD) is actu-ally investigated using processed filters based on gradient features and applied with a new way. A new convolutional layer is proposed through a sparse feature selection method known as Automated Negotiation-based Online Feature Selection (ANOFS). CNNAFD ends by stacked fully connected layers which represent a strong classifier. The fusion of CNNAFD and MobileNetV2 constructs the newnetwork CNNAFD-MobileNetV2 which improves the classification results and gives better detection decisions. Our work also introduces a new Tunisian Horse Detection Database (THDD). The proposed detector with the new CNNAFD-MobileNetV2 network achieved an average precision equal to 99.78%, 99% and 98.28% for cats, dogs and horses respectively.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

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