Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysis
This work addresses fairness and bias issues in face analysis applications, but appears incremental in combining existing SSL techniques with texture features.
The authors tackled the problem of robust and unbiased face analysis by using self-supervised learning (SSL) as an auxiliary task to blend texture-based local descriptors into feature modeling, with experiments showing improved feature representation for face attribute analysis, emotion analysis, and deepfake detection.
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.