A lightweight deep learning model for real-time face recognition

Abstract

Lightweight deep learning models for face recognition are becoming increasingly crucial for deployment on resource-constrained devices such as embedded systems or mobile devices. This paper presents a highly efficient and compact deep learning (DL) model that achieves state-of-the-art performance on various face recognition benchmarks. The developed DL model employs one- or few-shot learning to obtain effective feature embeddings and draws inspiration from FaceNet with significant refinements to achieve a memory size of only 3.5 MB—about 30 times smaller than FaceNet—while maintaining high accuracy and real-time performance. The study demonstrates the model's effectiveness through extensive experiments, which include testing on public datasets and the model's ability to recognize occluded faces in uncontrolled environments using grayscale input images. Compared to the state-of-the-art lightweight models, the proposed model requires fewer FLOPs (0.06G), has a smaller number of parameters (1.2 M), and occupies a smaller model size (3.5 MB) while achieving a competitive level of recognition accuracy and real-time performance. The results show that the model is well-suited for deployment in embedded domains, including live entrance security checks, driver authorization, and in-class attendance systems. The entire code of FN8 is available on GitHub.