  Cross-resolution face recognition (CRFR), which is important in intelligent
surveillance and biometric forensics, refers to the problem of matching a
low-resolution (LR) probe face image against high-resolution (HR) gallery face
images. Existing shallow learning-based and deep learning-based methods focus
on mapping the HR-LR face pairs into a joint feature space where the resolution
discrepancy is mitigated. However, little works consider how to extract and
utilize the intermediate discriminative features from the noisy LR query faces
to further mitigate the resolution discrepancy due to the resolution
limitations. In this study, we desire to fully exploit the multi-level deep
convolutional neural network (CNN) feature set for robust CRFR. In particular,
our contributions are threefold. (i) To learn more robust and discriminative
features, we desire to adaptively fuse the contextual features from different
layers. (ii) To fully exploit these contextual features, we design a feature
set-based representation learning (FSRL) scheme to collaboratively represent
the hierarchical features for more accurate recognition. Moreover, FSRL
utilizes the primitive form of feature maps to keep the latent structural
information, especially in noisy cases. (iii) To further promote the
recognition performance, we desire to fuse the hierarchical recognition outputs
from different stages. Meanwhile, the discriminability from different scales
can also be fully integrated. By exploiting these advantages, the efficiency of
the proposed method can be delivered. Experimental results on several face
datasets have verified the superiority of the presented algorithm to the other
competitive CRFR approaches.
