Dynamic Feature Learning for Partial Face Recognition

Partial face recognition (PFR) in unconstrained environment is a very important task, especially in video surveillance, mobile devices, etc. However, a few studies have tackled how to recognize an arbitrary patch of a face image. This study combines Fully Convolutional Network (FCN) with Sparse Repr...

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Bibliographic Details
Published in2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 7054 - 7063
Main Authors He, Lingxiao, Li, Haiqing, Zhang, Qi, Sun, Zhenan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Convolution
Face
Face recognition
Feature extraction
Microsoft Windows
Probes
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Summary:Partial face recognition (PFR) in unconstrained environment is a very important task, especially in video surveillance, mobile devices, etc. However, a few studies have tackled how to recognize an arbitrary patch of a face image. This study combines Fully Convolutional Network (FCN) with Sparse Representation Classification (SRC) to propose a novel partial face recognition approach, called Dynamic Feature Matching (DFM), to address partial face images regardless of size. Based on DFM, we propose a sliding loss to optimize FCN by reducing the intra-variation between a face patch and face images of a subject, which further improves the performance of DFM. The proposed DFM is evaluated on several partial face databases, including LFW, YTF and CASIA-NIR-Distance databases. Experimental results demonstrate the effectiveness and advantages of DFM in comparison with state-of-the-art PFR methods.
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00737