太原理工大学学报2018，Vol.49Issue(1)：106-112,7.DOI:10.16355/j.cnki.issn1007-9432tyut.2018.01.016
基于深度学习的大规模人脸图像检索
Large-Scale Face Image Retrieval based on Deep Residual Embedding Feature
摘要
Abstract
With the improvement of hardware,deep learning features overcome the weakness of traditional man-made feature such as poor robustness and complex retrieval calculation.A coarse-to-fine face image retrieval based on deep learning feature was proposed.First,a face feature extraction model is developed by using nearly four million face images to train the convolutional neural networks.Second,face feature is extracted,stored and clustered.Finally,face retrieval is performed by coarse-to-fine retrieval.Face verification gets a 99.1％ accuracy via deep learning face feature in the LFW benchmark and face retrieval costs only about 0.5 second in a million face retrieval benchmark.The experiment results illustrate that deep learning face feature is more robust and lower in computational complexity.The retrieval method from coarse to fine has high efficiency and high accuracy.关键词
人脸检索/卷积神经网络/深度学习/由粗到细Key words
face retrieval/convolutional neural networks/deep learning/coarse to fine分类
信息技术与安全科学引用本文复制引用
卢宗光,刘青山,孙玉宝..基于深度学习的大规模人脸图像检索[J].太原理工大学学报,2018,49(1):106-112,7.基金项目
国家自然科学基金资助项目(61672292) （61672292）
江苏省“六大人才高峰”高层次人才培养计划(DZXX-037) （DZXX-037）
