计算机应用与软件2024，Vol.41Issue(11)：228-233,6.DOI:10.3969/j.issn.1000-386x.2024.11.032
融合深度字典学习和特征重建的遮挡人脸检测研究
AN OCCLUSION FACE DETECTION METHOD COMBINING DEPTH DICTIONARY LEARNING AND FEATURE RECONSTRUCTION
摘要
Abstract
Aimed at the low accuracy of occluded face detection in complex scenes,an occlusion face detection method combining depth dictionary learning and feature reconstruction is proposed.A shallow CNN was used to generate face candidate regions,and the pre-trained VGG16 network was used to characterize them.A sparse coding method was used to establish a deep retrieval dictionary composed of typical faces and non-faces.Using the locality preserving projections method,the feature descriptor of the face candidate region was reconstructed into a similarity-based feature vector by using the retrieval dictionary.The reconstructed feature vector was sent to the deep neural network to perform face/non-face classification and face bounding box location regression simultaneously.The experimental results on the MAFA occlusion face dataset show that the detection accuracy of this method is about 12.3 percentage points higher than the current mainstream face detection method.关键词
人脸检测/遮挡人脸/卷积神经网络/字典学习/特征重建Key words
Face detection/Occlusion face/Convolutional neural network/Dictionary learning/Feature recon-struction分类
信息技术与安全科学引用本文复制引用
戴惠丽..融合深度字典学习和特征重建的遮挡人脸检测研究[J].计算机应用与软件,2024,41(11):228-233,6.基金项目
福建省教育厅中青年科研项目(JAT170873). （JAT170873）
