FaceLiVT: Face Recognition using Linear Vision Transformer with Structural Reparameterization For Mobile Device

• URL: http://arxiv.org/abs/2506.10361v1
• Date: Thu, 12 Jun 2025 05:36:40 GMT
• Title: FaceLiVT: Face Recognition using Linear Vision Transformer with Structural Reparameterization For Mobile Device
• Abstract summary: FaceLiVT is a lightweight yet powerful face recognition model.<n>It integrates a hybrid Convolution Neural Network (CNN)-Transformer architecture with an innovative and lightweight Multi-Head Linear Attention mechanism.
• Score: 3.617580194719686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces FaceLiVT, a lightweight yet powerful face recognition model that integrates a hybrid Convolution Neural Network (CNN)-Transformer architecture with an innovative and lightweight Multi-Head Linear Attention (MHLA) mechanism. By combining MHLA alongside a reparameterized token mixer, FaceLiVT effectively reduces computational complexity while preserving competitive accuracy. Extensive evaluations on challenging benchmarks; including LFW, CFP-FP, AgeDB-30, IJB-B, and IJB-C; highlight its superior performance compared to state-of-the-art lightweight models. MHLA notably improves inference speed, allowing FaceLiVT to deliver high accuracy with lower latency on mobile devices. Specifically, FaceLiVT is 8.6 faster than EdgeFace, a recent hybrid CNN-Transformer model optimized for edge devices, and 21.2 faster than a pure ViT-Based model. With its balanced design, FaceLiVT offers an efficient and practical solution for real-time face recognition on resource-constrained platforms.

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