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.
Abstract (translated)
本文介绍了FaceLiVT，这是一种轻量级但强大的面部识别模型，它结合了混合卷积神经网络（CNN）-变压器架构，并且采用了创新的、轻量级的多头线性注意力（MHLA）机制。通过将MHLA与重新参数化的标记混合器结合使用，FaceLiVT有效地降低了计算复杂度，同时保持了竞争力的准确性。在包括LFW、CFP-FP、AgeDB-30、IJB-B和IJB-C在内的具有挑战性的基准测试中进行了广泛的评估，结果显示其性能优于最先进的轻量级模型。MHLA显著提高了推理速度，使FaceLiVT能够在移动设备上以较低的延迟提供高精度的结果。具体而言，FaceLiVT的速度比为边缘设备优化的最近混合CNN-Transformer模型EdgeFace快8.6倍，比基于纯ViT的模型快21.2倍。凭借其平衡的设计，FaceLiVT为资源受限平台上的实时面部识别提供了高效且实用的解决方案。
URL
https://arxiv.org/abs/2506.10361
