Realtime Face Verification with Lightweight Convolutional Neural Networks

Face verification is a promising method for user authentication. Besides existing methods with deep convolutional neural networks to handle millions of people using powerful computing systems, the authors aim to propose an alternative approach of a lightweight scheme of convolutional neural networks (CNN) for face verification in realtime. Our goal is to propose a simple yet efficient method for face verification that can be deployed on regular commodity computers for individuals or small-to-medium organizations without super-computing strength. The proposed scheme targets unconstrained face verification, a typical scenario in reality. Experimental results on original data of Labeled Faces in the Wild dataset show that our best CNN found through experiments with 10 hidden layers achieves the accuracy of \((82.58 \pm 1.30)\,\%\) while many other instances in the same scheme can also approximate this result. The current implementation of our method can run at 60 fps and 235 fps on a regular computer with CPU-only and GPU configurations respectively. This is suitable for deployment in various applications without special requirements of hardware devices.