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<p>In this work, we benchmark with different backbones and study their impact for self-supervised Learning (SSL) as an auxiliary task to blend texture-based local descriptors into feature modelling for efficient face analysis. It is established in the previous work that combining a primary task and a self-supervised auxiliary task enables more robust and discriminative representation learning. We employed the different shallow to deep backbones for the SSL task of Masked Auto-Encoder (MAE) as an auxiliary objective to reconstruct texture features such as local patterns alongside the primary task in local pattern SSAT (L-SSAT), ensuring robust and unbiased face analysis. To expand the benchmark, we conducted a comprehensive comparative analysis across multiple model configurations within the proposed framework. To this end, we address the three research questions 'What is the role of the backbone in performance L-SSAT?', 'What type of backbone is effective for the different face analysis task?' and 'If there is any generalized backbone for effective face analysis with L-SSAT'. Towards answering these questions, we provide a detailed study and experiments. The performance evaluation demonstrates that the backbone for the proposed method is highly dependent on the downstream task achieving average accuracies of 0.94 on FaceForensics++, 0.87 on CelebA, and 0.88 on AffectNet. For consistency of feature representation quality and generalisation capability, across various face analysis paradigms, including face attribute prediction, emotion classification, and deepfake detection, there is no unified backbone.</p>

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A Backbone Benchmarking Study on Self-supervised Learning as a Auxiliary Task with Texture-Based Local Descriptors for Face Analysis

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Shukesh Reddy,
  • Abhijit Das

摘要

In this work, we benchmark with different backbones and study their impact for self-supervised Learning (SSL) as an auxiliary task to blend texture-based local descriptors into feature modelling for efficient face analysis. It is established in the previous work that combining a primary task and a self-supervised auxiliary task enables more robust and discriminative representation learning. We employed the different shallow to deep backbones for the SSL task of Masked Auto-Encoder (MAE) as an auxiliary objective to reconstruct texture features such as local patterns alongside the primary task in local pattern SSAT (L-SSAT), ensuring robust and unbiased face analysis. To expand the benchmark, we conducted a comprehensive comparative analysis across multiple model configurations within the proposed framework. To this end, we address the three research questions 'What is the role of the backbone in performance L-SSAT?', 'What type of backbone is effective for the different face analysis task?' and 'If there is any generalized backbone for effective face analysis with L-SSAT'. Towards answering these questions, we provide a detailed study and experiments. The performance evaluation demonstrates that the backbone for the proposed method is highly dependent on the downstream task achieving average accuracies of 0.94 on FaceForensics++, 0.87 on CelebA, and 0.88 on AffectNet. For consistency of feature representation quality and generalisation capability, across various face analysis paradigms, including face attribute prediction, emotion classification, and deepfake detection, there is no unified backbone.