Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157907
Title: Generalized multi-modal for face anti-spoofing
Authors: Muhammad Hazeeq Abdul Rahman
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Engineering::Electrical and electronic engineering
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Muhammad Hazeeq Abdul Rahman (2021). Generalized multi-modal for face anti-spoofing. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157907
Project: A3095-211
Abstract: In recent years, the research community has developed and proposed various face anti-spoofing models that involves multiple modalities. The demand for these methods continue to rise due to advancement of sensors and increasing usage of biometric security. However, there has been no research on compressed multi-modality face anti-spoofing methods that can offer good generalization performance. This project proposes a compressed multi-modality face anti-spoofing model based on an existing state-of-the-art method. The proposed model requires a lower amount of computational resource and has a much shorter inference time, suitable for deployment to edge devices. It manages to obtain comparable performance to that of the state-of-the-art face anti-spoofing model.
URI: https://hdl.handle.net/10356/157907
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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A final year report proposing an approach to compress face anti-spoofing methods to allow deployment to edge devices2.2 MBAdobe PDFView/Open

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