  A light-weight low-resolution face gender classification method, called
FaceHop, is proposed in this research. We have witnessed rapid progress in face
gender classification accuracy due to the adoption of deep learning (DL)
technology. Yet, DL-based systems are not suitable for resource-constrained
environments with limited networking and computing. FaceHop offers an
interpretable non-parametric machine learning solution. It has desired
characteristics such as a small model size, a small training data amount, low
training complexity, and low-resolution input images. FaceHop is developed with
the successive subspace learning (SSL) principle and built upon the foundation
of PixelHop++. The effectiveness of the FaceHop method is demonstrated by
experiments. For gray-scale face images of resolution $32 \times 32$ in the LFW
and the CMU Multi-PIE datasets, FaceHop achieves correct gender classification
rates of 94.63% and 95.12% with model sizes of 16.9K and 17.6K parameters,
respectively. It outperforms LeNet-5 in classification accuracy while LeNet-5
has a model size of 75.8K parameters.
