南京理工大学学报（自然科学版）2018，Vol.42Issue(1)：40-47,8.DOI:10.14177/j.cnki.32-1397n.2018.42.01.006
基于级联卷积神经网络的人脸检测算法
Face detection algorithm based on cascaded convolutional neural network
摘要
Abstract
To overcome the problem that most of the methods based on depth learning cannot get a balance between speed and accuracy when directly extracting the depth of abstract features. This paper proposes a face detection method based on cascade neural network by combining traditional cascade framework with from-shallow-to-deep convolutional neural network. Firstly,this paper selects candidate face regions by means of fusing the confidence maps of images with part and full faces based on full convolutional neural network. Secondly,this paper extracts robust features of face to validate the candidate regions. Simultaneously,this paper locates the face with combined regression to improve the detection accuracy. In the experiments,the proposed method achieves comparable or better accuracy and speed on FDDB,AFW benchmarks.关键词
人脸检测/级联结构/神经网络/全卷积网络/无约束条件Key words
face detection/cascade structure/neural network/fully convolutional networks/uncon-strained condition分类
信息技术与安全科学引用本文复制引用
孙康,李千目,李德强..基于级联卷积神经网络的人脸检测算法[J].南京理工大学学报（自然科学版）,2018,42(1):40-47,8.基金项目
国家重点研发计划政府间国际科技创新合作重点专项(S2016G9070) （S2016G9070）
中央高校基本科研业务费专项资金资助(30916015104) （30916015104）
