Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157482
Title: Facial spoofing indicator using deep learning
Authors: Lim, Eugen Wei Jie
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Lim, E. W. J. (2022). Facial spoofing indicator using deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157482
Project: A1168-211
Abstract: Moving along with advancements in the technology sector, biometric verification is becoming more and more common due to its simplicity and user-friendliness. Out of all the biometric verification, facial biometric verification is the most common. Facial biometric is linked with an increase in vulnerability to facial spoofing attacks as it is easy to acquire individuals’ photos from platforms such as social media or Google. Therefore, the aim of this project is to find ways on how to improve the current deep learning models by approaching photo attacks. Photo attack datasets were used to train the model and to test its accuracy by classifying 2 classes of images into fake and real. With the usage of RandAugment [20], it shows that the models can produce slightly better results than normal data augmentation.
URI: https://hdl.handle.net/10356/157482
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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