计算机工程与应用2019，Vol.55Issue(24)：184-189,6.DOI:10.3778/j.issn.1002-8331.1809-0212
级联的卷积神经网络人脸检测方法
Concatenated Convolutional Neural Network Face Detection Method
摘要
Abstract
Aiming at the problem of low face detection accuracy caused by changes in lighting, low resolution, posture and expression, and the generalization of algorithms caused by most face detection algorithms using a single convolutional neural network to extract features, a three-layer convolutional neural network structure consisting of shallow and deep cas-cade is proposed. The face candidate region is quickly located by the full convolutional neural network. Then the depth neural network is used to extract the face robustness feature, and the candidate region is further classified and verified. The joint regression face method is used to determine the final face position and improve the detection accuracy. At the same time, the commonly used non-maximum value suppression method is improved by weighting the reduction score, and the missed detection problem caused by the overlapping of adjacent faces is solved. The experimental results show that the model is robust to the above-mentioned factors that cause low face detection accuracy, and it has high accuracy and running speed in FDDB dataset. The improved non-maximum suppression algorithm also has a certain improvement on the test accuracy of FDDB.关键词
人脸检测/全卷积网络/联合回归Key words
face detection/full convolutional network/joint regression分类
信息技术与安全科学引用本文复制引用
李亚可,玉振明..级联的卷积神经网络人脸检测方法[J].计算机工程与应用,2019,55(24):184-189,6.基金项目
广西重点研发计划(No.桂科AB16380273) （No.桂科AB16380273）
国家自然科学基金(No.61562074). （No.61562074）
