.png){kind=icon}
返回
Face detection and tracking using hybrid margin-based ROI techniques
.png){kind=icon}
PRE
AI
DOI：10.1007/s00371-019-01649-y.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This study is to solve the problem of low accuracy and slow processing speed for real-time face detection and tracking systems. A margin-based region of interest approach with fixed and dynamic margin concepts is proposed to speed up the processing time. In addition, a hybrid system is developed to boost the accuracy and overcome the deficiency of the main detection algorithm. This approach consists of two routines, i.e., main and escape routines. Three algorithms are used independently as the main routine to evaluate the effectiveness of the proposed hybrid approach. These algorithms are Haar cascade, Joint cascade, and multitask convolutional neural networks. The escape routine based on template matching algorithm is designed to evaluate the effectiveness of the proposed hybrid approach and improve detection accuracy. Two RGB video datasets with diversity and variations in face poses, video backgrounds, illuminations, video resolutions, expressions, over exposed faces, and occlusions of people within various unseen environments have been used for experiments and evaluation. The experiment results confirm that the hybrid approach is capable of detecting and tracking faces in non-frontal orientation with better accuracy and faster processing speed, i.e., four times faster than the conventional full frame scanning techniques.
Keyword:
Face detection
Joint cascade
Convolutional neural network
Haar cascade
Template matching
Region of interest
Hybrid model
Dynamic margin
Face tracking
Processing time
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.9
论文数:
4.6K
被引数:
6.5K
机构
引用论文
分享收藏A survey on face modeling: building a bridge between face analysis and synthesis.png)
VISUAL COMPUTER
VISUAL COMPUTERIF2.9
PREAI
分享收藏
Investigation of Ti mesh-supported anodes for direct borohydride fuel cells直接硼氢化物燃料电池用钛网支撑阳极的研究
PREAI
分享收藏分享收藏
查看更多内容