  A non-parametric low-resolution face recognition model for
resource-constrained environments with limited networking and computing is
proposed in this work. Such environments often demand a small model capable of
being effectively trained on a small number of labeled data samples, with low
training complexity, and low-resolution input images. To address these
challenges, we adopt an emerging explainable machine learning methodology
called successive subspace learning (SSL).SSL offers an explainable
non-parametric model that flexibly trades the model size for verification
performance. Its training complexity is significantly lower since its model is
trained in a one-pass feedforward manner without backpropagation. Furthermore,
active learning can be conveniently incorporated to reduce the labeling cost.
The effectiveness of the proposed model is demonstrated by experiments on the
LFW and the CMU Multi-PIE datasets.
