计算机工程与应用2019，Vol.55Issue(5)：124-128,165,6.DOI:10.3778/j.issn.1002-8331.1805-0034
全卷积神经网络的多尺度人脸检测的研究
Multi-Scale Face Detection of Full Convolution Neural Network
摘要
Abstract
In order to achieve fast and accurate face detection, a multi-scale face detection method based on full Convolu-tional Neural Network(CNN)is proposed. The full connectivity layer of the convolutional neural network model AlexNet is changed to full convolution layer, and divided the layer into two categories of face and non-face, the accuracy after training as high as 99.16%.When the trained classification model is used for face detection, the image to be detected is input to the full convolutional network through multi-scale transformation to obtain the probability characteristic figure, and the most accurate face frame is obtained by the inhibition of non-maximal value. The test results show that this method has the advantages of high accuracy, short detection time and good performance in face detection.关键词
卷积神经网络/人脸检测/AlexNet/多尺度变换Key words
convolutional neural network/ face detection/ AlexNet/ multi-scale transformation分类
计算机与自动化引用本文复制引用
罗明柱,肖业伟..全卷积神经网络的多尺度人脸检测的研究[J].计算机工程与应用,2019,55(5):124-128,165,6.基金项目
国家自然科学基金(No.61572142) （No.61572142）
广东省科技计划(No.2016B030306004,No.2016A010101027) （No.2016B030306004,No.2016A010101027）
广州市科技计划(No.201605101034176). （No.201605101034176）
