https://scholars.lib.ntu.edu.tw/handle/123456789/413039
標題: | Deep disguised faces recognition | 作者: | Zhang K. Chang Y.-L. Hsu W. |
公開日期: | 2018 | 卷: | 2018-June | 起(迄)頁: | 32-36 | 來源出版物: | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops | 摘要: | Recently, deep learning based approaches have yielded a significant improvement in face recognition in the wild. However,' disguised face' recognition is still a challenging task that needs to be investigated, and the Disguised Faces in the Wild (DFW) competition is designed for this task. In this paper, we propose a two-stage training approach to utilize the small-scale training data provided by the DFW competition. Specifically, in the first stage, we train Deep Convolutional Neural Networks (DCNNs) for generic face recognition. In the second stage, we use Principal Components Analysis (PCA) based on the DFW training set to find the best transformation matrix for identity representation of disguised faces. We evaluate our model on the DFW testing dataset and it shows better performance over the state-of-the-art generic face recognition methods. It also achieves the best results on the DFW competition-Phase 1. ? 2018 IEEE. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413039 | ISBN: | 9781538661000 | ISSN: | 21607508 | DOI: | 10.1109/CVPRW.2018.00012 |
顯示於: | 資訊工程學系 |
檔案 | 描述 | 大小 | 格式 | |
---|---|---|---|---|
Zhang_Deep_Disguised_Faces_CVPR_2018_paper.pdf | 929.9 kB | Adobe PDF | 檢視/開啟 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。