Non-frontal face recognition using GANs and memristor-based classifiers
It addresses the problem of efficient non-frontal face recognition for resource-constrained edge devices like drones.
The paper proposes a facial recognition framework that uses lightweight GAN-based pose frontalisation combined with memristor-based neuromorphic classifiers to handle non-frontal faces, achieving up to 96% identification accuracy on two datasets.
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.