Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach

Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach

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Makeup modifies facial textures and colors, impacting the precision of face anti-spoofing systems.Many individuals opt for light makeup in their daily lives, which generally does not hinder face identity recognition.However, current research in face anti-spoofing often neglects the influence of light makeup on facial feature recognition, notably the absence of publicly accessible datasets toyo proxes st iii 305/40r22 featuring light makeup faces.If these instances are incorrectly flagged as fraudulent by face anti-spoofing systems, it could lead to user inconvenience.In response, we develop a face anti-spoofing database that includes light makeup faces and establishes a criterion for determining light makeup to select appropriate data.

Building on this foundation, we assess multiple established face anti-spoofing algorithms using the newly created database.Our findings reveal that the majority of these algorithms experience a decrease in performance when faced with light makeup faces.Consequently, this ole miss stripe out paper introduces a general face anti-spoofing algorithm specifically designed for light makeup faces, which includes a makeup augmentation module, a batch channel normalization module, a backbone network updated via the Exponential Moving Average (EMA) method, an asymmetric virtual triplet loss module, and a nearest neighbor supervised contrastive module.The experimental outcomes confirm that the proposed algorithm exhibits superior detection capabilities when handling light makeup faces.