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

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.

@article{setyawan2025_2506.10361,
  title={ FaceLiVT: Face Recognition using Linear Vision Transformer with Structural Reparameterization For Mobile Device },
  author={ Novendra Setyawan and Chi-Chia Sun and Mao-Hsiu Hsu and Wen-Kai Kuo and Jun-Wei Hsieh },
  journal={arXiv preprint arXiv:2506.10361},
  year={ 2025 }
}