Zhaoyuan “Andy” Fang

I am MS student in Robotics at Carnegie Mellon University, working with Prof. Katerina Fragkiadaki on Computer Vision and Machine Learning.

I got my undergraduate degree from the University of Notre Dame, majoring in Electrical Engineering and Maths. At Notre Dame, I am fortunate to have spent great time working with Prof. Adam Czajka and Prof. Kevin Bowyer. During my undergrad, I was lucky to have interned with Prof. David Held and collaborated with Prof. Hang Zhao.

Email  /  CV  /  Google Scholar  /  Github

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I'm interested in and currently working on computer vision and machine learning. Throughout my undergrad, I worked mostly on Biometrics, while my first research experience is on nanophotonics. Some works can be found below (* indicates equal contribution). For a full list please visit Google Scholar.

Robust Iris Presentation Attack Detection Fusing 2D and 3D Information
Zhaoyuan Fang, Adam Czajka, Kevin W. Bowyer
IEEE Transactions on Information Forensics and Security (T-IFS), 2020
code / video

Experiments show that 2D textual and 3D shape features are complementary for iris presentation attack detecttion and fusing then together results in robust performance under various open-set testing scenarios.

Open Source Iris Recognition Hardware and Software with Presentation Attack Detection
Zhaoyuan Fang, Adam Czajka
IEEE International Joint Conference on Biometrics (IJCB), 2020
code / video

An open source, low-cost, fast and accuract iris recognition protoype with presentation attack detection based on Raspberry-Pi and Python.

GSIR: Generalizable 3D Shape Interpretation and Reconstruction
Jianren Wang, Zhaoyuan Fang
European Conference on Computer Vision (ECCV), 2020
project page / video

A model designed for joint shape interpretation and reconstruction improves performance on both tasks.

AlignNet: A Unifying Approach to Audio-Visual Alignment
Jianren Wang*, Zhaoyuan Fang*, Hang Zhao
IEEE Winter Conf. on Applications of Computer Vision (WACV), 2020
project page / code / video

End-to-end dense correspondence between each frame of a video and an audio can be learned with a model with simple and well-established principles: attention, pyramidal processing, warping, and affinity function.

Non-Intrusive Appliance Identification with Appliance-Specific Networks
Zhaoyuan Fang, Dongbo Zhao, Chen Chen, Yang Li, Yuting Tian
IEEE Transactions on Industry Appliances (T-IA), 2020

Lightweight appliance specific networks breaks down the load identification problem into easier subproblems.

Iris Presentation Attack Detection Based on Photometric Stereo Features
Adam Czajka, Zhaoyuan Fang, Kevin W. Bowyer
IEEE Winter Conf. on Applications of Computer Vision (WACV), 2019

Traditional 3D reconstruction technique Photometric Stereo provides surprisingly simple but effective features to classify real iris images from fake ones.


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