用自监督学习增强纹理特征，让人脸识别更鲁棒公平。
Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysis
- 以MAE为辅助任务重建局部纹理特征
- 在人脸属性、情绪分析和深度伪造检测中提升表现
- 适合需要消除偏见的公平性人脸识别场景
本文探索将自监督学习（SSL）作为辅助任务，融合基于纹理的局部描述符到特征建模中，以实现高效的面部分析。通过将主任务与自监督辅助任务结合，有助于构建鲁棒的表示。我们采用掩码自编码器（MAE）作为辅助任务，重建如局部模式等纹理特征，同时进行主任务训练，以实现鲁棒且无偏的人脸分析。我们在三种主流人脸分析范式上验证了该方法：人脸属性分析、基于表情的人脸分析以及深度伪造检测。实验结果表明，所提模型能获得更优的特征表示，支持公平且无偏的人脸分析。
原文摘要 · Abstract (English)
In this work, we explore Self-supervised Learning (SSL) as an auxiliary task to blend the texture-based local descriptors into feature modelling for efficient face analysis. Combining a primary task and a self-supervised auxiliary task is beneficial for robust representation. Therefore, we used the SSL task of mask auto-encoder (MAE) as an auxiliary task to reconstruct texture features such as local patterns along with the primary task for robust and unbiased face analysis. We experimented with our hypothesis on three major paradigms of face analysis: face attribute and face-based emotion analysis, and deepfake detection. Our experiment results exhibit that better feature representation can be gleaned from our proposed model for fair and bias-less face analysis.
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