A Face Detection Method Based on Cascade Convolutional Neural Network

被引：9

作者：

Yang, Wankou ^{[1
,2
]}

Zhou, Lukuan ^{[1
,2
]}

Li, Tianhuang ^{[1
,2
]}

Wang, Haoran ^{[3
]}

机构：

[1] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China

[2] Minist Educ, Key Lab Measurement & Control Complex Syst Engn, Nanjing 210096, Jiangsu, Peoples R China

[3] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China

来源：

MULTIMEDIA TOOLS AND APPLICATIONS | 2019年 / 78卷 / 17期

关键词：

Face detection; Cascade convolution structure; Soft non-maximum suppression;

D O I：

10.1007/s11042-018-6995-0

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：24373 / 24390

页数：18