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 Representation Classification (SRC) to propose a novel partial face recognition approach, called Dynamic Feature Matching (DFM), to address partial face images regardless of sizes. 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.
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