Submission achieves the Average Classification Error Rate (ACER) of 2.21 with Method wins the third place in the final ranking of Chalearn Multi-modalĬross-ethnicity Face Anti-spoofing Recognition Our final To ensure stable and accurate prediction for video classification. (SMP) is designed to enable a customized pipeline for each data modality to
Unlike previous works, Selective Modal Pipeline Novel pipeline-based multi-stream CNN architecture called PipeNet for They will see your trademark, the goods and services on your registration, the date you applied for trademark registration, and the date your trademark registered. This provides public notice to anyone searching for similar trademarks. and advertisements for the Bull Durham brand of smoking tobacco manufactured by a North Carolina firm in the. Subjects, and 2D plus 3D attack types in four protocols, and focusing on theĬhallenge of improving the generalization capability of face anti-spoofing inĬross-ethnicity and multi-modal continuous data. Trademark is listed in our database of registered and pending trademarks. Addressing the shortage of multi-modal face dataset,ĬASIA recently released the largest up-to-date CASIA-SURF Cross-ethnicity FaceĪnti-spoofing(CeFA) dataset, covering 3 ethnicities, 3 modalities, 1607
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Authors: Qing Yang, Xia Zhu, Jong-Kae Fwu, Yun Ye, Ganmei You, Yuan Zhu Download PDF Abstract: Face anti-spoofing has become an increasingly important and critical securityįeature for authentication systems, due to rampant and easily launchable All other names and services mentioned in this manual that are trademarks, registered trademarks, or service marks, are the property of their respective owners.