Z
Zitong Yu
Researcher at University of Oulu
Publications - 116
Citations - 2535
Zitong Yu is an academic researcher from University of Oulu. The author has contributed to research in topics: Computer science & Spoofing attack. The author has an hindex of 19, co-authored 85 publications receiving 1017 citations. Previous affiliations of Zitong Yu include Capital Normal University & Murdoch University.
Papers
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Proceedings ArticleDOI
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
TL;DR: Yu et al. as discussed by the authors proposed a frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information.
Proceedings ArticleDOI
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing
TL;DR: A new approach to detect presentation attacks from multiple frames based on two insights, able to capture discriminative details via Residual Spatial Gradient Block (RSGB) and encode spatio-temporal information from Spatio-Temporal Propagation Module (STPM) efficiently.
Proceedings ArticleDOI
Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement
TL;DR: Wang et al. as discussed by the authors proposed a two-stage, end-to-end method using hidden rPPG information enhancement and attention networks, which is the first attempt to counter video compression loss and recover rPGP signals from highly compressed videos.
Journal ArticleDOI
NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing
TL;DR: This work proposes the first FAS method based on neural architecture search (NAS), called NAS-FAS, to discover the well-suited task-aware networks, and develops a novel search space consisting of central difference convolution and pooling operators.
Posted Content
Remote Heart Rate Measurement from Highly Compressed Facial Videos: an End-to-end Deep Learning Solution with Video Enhancement
TL;DR: Comprehensive experiments are performed to show that the proposed method not only achieves superior performance on compressed videos with high-quality videos pair, but also generalizes well on novel data with only compressed videos available, which implies the promising potential for real-world applications.