GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations

• Published in: IEEE access Vol. 11; pp. 35429 - 35446
• Main Authors: Alansari, Mohamad, Hay, Oussama Abdul, Javed, Sajid, Shoufan, Abdulhaid, Zweiri, Yahya, Werghi, Naoufel
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: ArcFace; Artificial neural networks; attention mechanism; Benchmarks; cheap operations; Computational complexity; Computational modeling; Computer architecture; Convolutional neural networks; Face recognition; Feature maps; GhostNet; Image classification; Lightweight; Linear transformations; Machine learning; Memory devices; Modules; Redundancy; Task analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3266068

The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge. Previous approaches to this problem have not prioritized the reduction of feature map redundancy, but the introduction of Ghost modules represents a major innovation in this area. Ghost modules use a series of inexpensive linear transformations to extract additional feature maps from a set of intrinsic features, allowing for a more comprehensive representation of the underlying information. GhostNetV1 and GhostNetV2, both of which are based on Ghost modules, serve as the foundation for a group of lightweight face recognition models called GhostFaceNets. GhostNetV2 expands upon the original GhostNetV1 by adding an attention mechanism to capture long-range dependencies. Evaluation of GhostFaceNets using various benchmarks reveals that these models offer superior performance while requiring a computational complexity of approximately 60-275 MFLOPs. This is significantly lower than that of State-Of-The-Art (SOTA) big convolutional neural network (CNN) models, which can require hundreds of millions of FLOPs. GhostFaceNets trained with the ArcFace loss on the refined MS-Celeb-1M dataset demonstrate SOTA performance on all benchmarks. In comparison to previous SOTA mobile CNNs, GhostFaceNets greatly improve efficiency for face verification tasks. The GhostFaceNets code is available at: https://github.com/HamadYA/GhostFaceNets .