  Deep learning-based face recognition models follow the common trend in deep
neural networks by utilizing full-precision floating-point networks with high
computational costs. Deploying such networks in use-cases constrained by
computational requirements is often infeasible due to the large memory required
by the full-precision model. Previous compact face recognition approaches
proposed to design special compact architectures and train them from scratch
using real training data, which may not be available in a real-world scenario
due to privacy concerns. We present in this work the QuantFace solution based
on low-bit precision format model quantization. QuantFace reduces the required
computational cost of the existing face recognition models without the need for
designing a particular architecture or accessing real training data. QuantFace
introduces privacy-friendly synthetic face data to the quantization process to
mitigate potential privacy concerns and issues related to the accessibility to
real training data. Through extensive evaluation experiments on seven
benchmarks and four network architectures, we demonstrate that QuantFace can
successfully reduce the model size up to 5x while maintaining, to a large
degree, the verification performance of the full-precision model without
accessing real training datasets.
