An Improved Neural Network Cascade for Face Detection in Large Scene 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 reso...

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Main Authors: Chengbin Peng, Wei Bu, Jiangjian Xiao, Ka-chun Wong, Minmin Yang
Format: Article
Language:English
Published: MDPI AG 2018-11-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/8/11/2222
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author Chengbin Peng
Wei Bu
Jiangjian Xiao
Ka-chun Wong
Minmin Yang
author_facet Chengbin Peng
Wei Bu
Jiangjian Xiao
Ka-chun Wong
Minmin Yang
author_sort Chengbin Peng
collection DOAJ
description 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.
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spelling doaj.art-df1b03778a38420eaa11422621ed15d02022-12-21T17:57:03ZengMDPI AGApplied Sciences2076-34172018-11-01811222210.3390/app8112222app8112222An Improved Neural Network Cascade for Face Detection in Large Scene SurveillanceChengbin Peng0Wei Bu1Jiangjian Xiao2Ka-chun Wong3Minmin Yang4Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo 315201, ChinaNingbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo 315201, ChinaNingbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo 315201, ChinaDepartment of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong, ChinaSchool of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315201, ChinaFace 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.https://www.mdpi.com/2076-3417/8/11/2222neural networknetwork cascadeslarge scene face detection
spellingShingle Chengbin Peng
Wei Bu
Jiangjian Xiao
Ka-chun Wong
Minmin Yang
An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance
Applied Sciences
neural network
network cascades
large scene face detection
title An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance
title_full An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance
title_fullStr An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance
title_full_unstemmed An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance
title_short An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance
title_sort improved neural network cascade for face detection in large scene surveillance
topic neural network
network cascades
large scene face detection
url https://www.mdpi.com/2076-3417/8/11/2222
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