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Race Bias Analysis of Bona Fide Errors in face anti-spoofing
The study of bias in Machine Learning is receiving a lot of attention in recent years, however, few only papers deal explicitly with the problem of race bias in face anti-spoofing.
Use of in-the-wild images for anomaly detection in face anti-spoofing
The traditional approach to face anti-spoofing sees it as a binary classification problem, and binary classifiers are trained and validated on specialized anti-spoofing databases.
A study of the effect of the illumination model on the generation of synthetic training datasets
Our results show that the effect of the illumination model is important, comparable in significance to the network architecture.
Using theoretical ROC curves for analysing machine learning binary classifiers
Most binary classifiers work by processing the input to produce a scalar response and comparing it to a threshold value.
Watermark retrieval from 3D printed objects via synthetic data training
We conclude that in our application domain of information retrieval from 3D printed objects, where access to the exact CAD files of the printed objects can be assumed, one can use inexpensive synthetic data to enhance neural network training, reducing the need for the labour intensive process of creating large amounts of hand labelled real data or the need to generate photorealistic synthetic data.
Watermark Retrieval from 3D Printed Objects via Convolutional Neural Networks
We present a method for reading digital data embedded in planar 3D printed surfaces.
