Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158548
Title: Neural network pruning for face anti-spoofing
Authors: Zhang, Yinan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Zhang, Y. (2022). Neural network pruning for face anti-spoofing. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158548
Abstract: With the development of smartphones and mobile payments, face recognition systems have been rapidly deployed and face anti-spoofing research has become increasingly popular due to many attacks which are threatening the security of users' data. The generalization performance of the face anti-spoofing(FAS) model is worth doing research due to the threats from unseen and different types from trained types of attacks. The popularity of wearable devices has more requirements: smaller volume, fewer parameters, and faster inference speed, for the FAS model due to the limitation of device computing power and volume. Model pruning is a very popular way of model compression, but the previous model pruning did not pay attention to the generalization ability of the pruned model. Therefore, we explore the effect of model pruning on the generalization capability of face anti-spoofing models and propose Meta-MMD based on MetaPruning and Maximum Mean Discrepancy(MMD) for Face anti-spoofing. After testing on the mainstream FAS datasets, the generalization performance increases after Meta-MMD pruning compared with using Metapruning.
URI: https://hdl.handle.net/10356/158548
Schools: School of Electrical and Electronic Engineering 
Research Centres: Rapid-Rich Object Search (ROSE) Lab 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Dissertation-Zhang YiNan.pdf
  Restricted Access
5.13 MBAdobe PDFView/Open

Page view(s)

157
Updated on Feb 23, 2024

Download(s)

15
Updated on Feb 23, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.