用生成模型和忆阻器芯片实现低功耗非正脸人脸识别
Non-frontal face recognition using GANs and memristor-based classifiers
- 用轻量GAN将侧脸图像转为正脸
- 结合忆阻器电路识别，准确率达96%
- 适合无人机等边缘设备实时使用
人脸识别系统通过深度学习技术取得了显著进展，在复杂场景中表现出高精度和鲁棒性。然而，这些方法计算开销大，难以在资源受限的平台（如无人机）上直接部署，尤其面对非正脸图像时挑战更大。忆阻器类脑系统作为边缘AI应用的新方案，具备生物启发式处理能力和高效可扩展的计算特性。本文提出一种融合轻量级生成对抗网络（GAN）姿态正脸化与忆阻器类脑识别的面部识别框架，有效应对非正脸姿态变化。在两个数据集上的实验结果表明，该方法结合对抗学习与忆阻技术具有显著优势，最高识别准确率达96%。所提方案缓解了传统AI的计算瓶颈，为动态现实环境中的面部识别提供了可扩展、高效的解决方案。
原文摘要 · Abstract (English)
Face recognition systems have advanced significantly through deep learning techniques, delivering high performance and robustness in complex scenarios. However, these approaches incur substantial computational overhead, limiting their in situ applicability in resource-constrained platforms such as drones, where they can address challenges including non-frontal facial imagery. Memristor-based neuromorphic systems have emerged as a compelling approach for edge AI applications, combining biologically inspired processing with efficient and scalable computation. In this work, we propose a facial recognition framework that addresses non-frontal pose variations by integrating lightweight generative adversarial network (GAN)-based pose frontalisation with memristor-based neuromorphic recognition. The experimental results on two datasets demonstrate the effectiveness of combining adversarial learning with memristive technology, achieving up to 96% identification accuracy. The proposed approach alleviates the computational bottlenecks of conventional AI and offers a scalable, efficient solution for face recognition in dynamic real-world environments.
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