ConvFaceNeXt: Lightweight Networks for Face Recognition

Seng Chun Hoo, Haidi Ibrahim () and Shahrel Azmin Suandi
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Seng Chun Hoo: School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia
Haidi Ibrahim: School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia
Shahrel Azmin Suandi: School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia

Mathematics, 2022, vol. 10, issue 19, 1-28

Abstract: The current lightweight face recognition models need improvement in terms of floating point operations (FLOPs), parameters, and model size. Motivated by ConvNeXt and MobileFaceNet, a family of lightweight face recognition models known as ConvFaceNeXt is introduced to overcome the shortcomings listed above. ConvFaceNeXt has three main parts, which are the stem, bottleneck, and embedding partitions. Unlike ConvNeXt, which applies the revamped inverted bottleneck dubbed the ConvNeXt block in a large ResNet-50 model, the ConvFaceNeXt family is designed as lightweight models. The enhanced ConvNeXt (ECN) block is proposed as the main building block for ConvFaceNeXt. The ECN block contributes significantly to lowering the FLOP count. In addition to the typical downsampling approach using convolution with a kernel size of three, a patchify strategy utilizing a kernel size of two is also implemented as an alternative for the ConvFaceNeXt family. The purpose of adopting the patchify strategy is to reduce the computational complexity further. Moreover, blocks with the same output dimension in the bottleneck partition are added together for better feature correlation. Based on the experimental results, the proposed ConvFaceNeXt model achieves competitive or even better results when compared with previous lightweight face recognition models, on top of a significantly lower FLOP count, parameters, and model size.

Keywords: face recognition; face verification; lightweight model; inverted residual block; ConvNeXt block; enhanced ConvNeXt block; ConvFaceNeXt (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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