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.

翻译：人脸识别系统通过深度学习技术取得了显著进展，在复杂场景中展现出高性能和鲁棒性。然而，这些方法带来了巨大的计算开销，限制了其在资源受限平台（如无人机）上的原位适用性，而此类平台恰能应对非正面人脸图像等挑战。基于忆阻器的神经形态系统已成为边缘人工智能应用中一种引人注目的方法，它将生物启发式处理与高效可扩展的计算相结合。在本工作中，我们提出了一种人脸识别框架，通过整合轻量级生成对抗网络（GAN）姿态正面化技术与基于忆阻器的神经形态识别，来处理非正面姿态变化。在两个数据集上的实验结果表明，对抗学习与忆阻技术的结合具有有效性，最高可实现96%的识别准确率。所提出的方法缓解了传统人工智能的计算瓶颈，为动态现实环境中的人脸识别提供了一种可扩展、高效的解决方案。