GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations

Mohamad Alansari, Oussama Abdul Hay, Sajid Javed, Abdulhaid Shoufan, Yahya Zweiri, Naoufel Werghi

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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:

Original languageBritish English
Pages (from-to)35429-35446
Number of pages18
JournalIEEE Access
StatePublished - 2023


  • ArcFace
  • attention mechanism
  • cheap operations
  • face recognition
  • GhostNet
  • lightweight


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