VarKFaceNet: An Efficient Variable Depthwise Convolution Kernels Neural Network for Lightweight Face Recognition

Abstract

We revisit the design of convolutional kernels in lightweight convolutional neural networks, and inspired by the recent advances in RepLKNet, we design a Variable Kernel Convolutional Network module VarKNet, which solves the problem of the imbalance between depthwise convolution and pointwise convolution in the case of depthwise separable convolution when the network width is large, and enriches the model’s receptive field. The VarKNet module adopts a multi-branch structure during training and is re-parameterized and fused into a single-path structure during inference to maintain the strong expressive ability of the model and improve the inference speed. In order to further enhance the information exchange between channels, VarKNet adds channel shuffling in the fused branches. Built on VarKNet, we designed a large-scale face recognition network VarKFaceNet. VarKFaceNet achieved A great achievement of 99.5% accuracy on the LFW dataset with 0.7M parameters and 0.24 GFLOPS. At the same time, the measured speed on the NVIDIA Jetson Nano platform is 159 times, 4.2 times, and 2.4 times that of ResNet-50, EfficientNet, and MobileFaceNet, respectively. VarKFaceNet excels in balancing speed and accuracy and is quite suitable for embedded devices with limited resources.

Keywords

• Face recognition
• local features
• multi-scale
• lightweight network