Abstract
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