.png){kind=icon}
返回
A Face Detection Method Based on Cascade Convolutional Neural Network
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-018-6995-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Cascade has been widely used in face detection where classifier with low computational cost can be firstly used to shrink most of the background while keeping the recall. In this paper, a new cascaded convolutional neural network method consisting of two main steps is proposed. During the first stage, low-pixel candidate window is used as an input such that the shallow convolutional neural network quickly extracts the candidate window. In the second stage, the window from the former stage is resized and used as an input to the corresponding network layer respectively. During the training period, joint online training is conducted for hard samples and the soft non-maximum suppression algorithm is used to test on the dataset. The whole network achieves improved performance on the FDDB and PASCAL face datasets.
Keyword:
Face detection
Cascade convolution structure
Soft non-maximum suppression
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:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Comparative study of layer by layer assembled multilayer films based on graphene oxide and reduced graphene oxide on flexible polyurethane foam: flame retardant and smoke suppression properties.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Performance Comparison and Application of Swarm Intelligence Algorithms in Crowd Evacuation群智能算法在人群疏散中的性能比较与应用
PREAI
分享收藏
查看更多内容