首页|New Computational Intelligence Study Findings Reported from Xi’an Jiaotong Liver pool University (Learning Inter and Intra Class Variation With Deep Frequency Factorization Network for Face Anti-spoofing)
New Computational Intelligence Study Findings Reported from Xi’an Jiaotong Liver pool University (Learning Inter and Intra Class Variation With Deep Frequency Factorization Network for Face Anti-spoofing)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning - Computational Intelligence. According to news reporting out of Suzhou, People’s Republic of China, by NewsRx editors, research stated, “In this paper, we aim to enhance the generalization ability of single-shot face anti-spoofing tasks in two aspects: 1) we establish a deep frequency factorization network ( DFF-Net) to capture deep frequency information, enabling better discrimination of multiple styles of face spoofing, including both 2D and 3D methods; 2) aided by a sample redistribution strategy, we construct a unified anomaly metric-based supervision system to address unknown face attacks. Specifically, the proposed D FF-Net explicitly extracts spoofing cues from the high, low, and fusion frequency domains by embedding a deep frequency filter module into each frequency branch .”
SuzhouPeople’s Republic of ChinaAsiaComputational IntelligenceMachine LearningCybersecurityXi’an Jiaotong Liverpool University
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