Realistic low-resolution (LR) face images refer to those captured by the real-world surveillance cameras at extreme standoff distances, thereby LR and poor in quality essentially. Owing to severe scarcity of labeled data, a high-capacity deep convolution neural networks (CNN) is hardly trained to confront the realistic LR face recognition (LRFR) challenge. We introduce in this letter a dual-stream mutual information distillation network (MIND-Net), whereby the non-identity specific mutual information (MI) characterized by generic face features coexistent on realistic and synthetic LR face images are distilled to render a resolution-invariant embedding space for LRFR. For a thorough analysis, we quantify the degree of MI distillation in terms normalized MI index. Our experimental results on the realistic LR face datasets substantiate that the MIND-Net instances assembled from the pre-learned CNNs stand out from the baselines and other state of the arts by a notable margin.
|Number of pages||5|
|Journal||IEEE Signal Processing Letters|
|Publication status||Published - 2021|
Bibliographical notePublisher Copyright:
© 1994-2012 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Applied Mathematics
- Electrical and Electronic Engineering