失败
计算机科学
卷积神经网络
人工智能
面部识别系统
特征(语言学)
面子(社会学概念)
冗余(工程)
深度学习
生物识别
集合(抽象数据类型)
编码(集合论)
机器学习
模式识别(心理学)
并行计算
哲学
社会科学
语言学
社会学
程序设计语言
操作系统
作者
Mohamad Alansari,Oussama Abdul Hay,Sajid Javed,Abdulhadi Shoufan,Yahya Zweiri,Naoufel Werghi
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:11: 35429-35446
被引量:9
标识
DOI:10.1109/access.2023.3266068
摘要
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 entire code will be made available after publication.
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