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Abstract <p>Face recognition systems have been substantially impacted by the COVID-19 pandemic, as masks cover crucial facial features that renders conventional techniques difficult to implement. A novel methodology for occluded face recognition is presented in the study, based on two-way transfer learning with soft decision fusion. In this approach, pairwise combinations of five pretrained models, namely, ResNet-50, Xception, EfficientNet-B0, DenseNet-121, and MobileNetV2, are used to extract pretrained embeddings of facial images from the 5-Celebrity-Faces dataset that is augmented with a synthetic mask artificially placed on each facial image. These embeddings are passed to three machine learning classifiers, namely, categorical boosting, logistic regression, and linear discriminant analysis. The posterior class probabilities of individual classifiers for pairwise model combinations are fused by averaging to gain highly accurate classification results. The experimental results show that the soft decision fusion of MobileNetV2 and DenseNet-121 outperforms all other models with the maximum accuracy of 91.67% achieved using the linear discriminant analysis classifier.</p>

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Unified Deep Learning Framework with Two-Way Transfer Learning for Occluded Faces: A Soft Decision Fusion Approach

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Ashi Agarwal,
  • Seba Susan

摘要

Abstract

Face recognition systems have been substantially impacted by the COVID-19 pandemic, as masks cover crucial facial features that renders conventional techniques difficult to implement. A novel methodology for occluded face recognition is presented in the study, based on two-way transfer learning with soft decision fusion. In this approach, pairwise combinations of five pretrained models, namely, ResNet-50, Xception, EfficientNet-B0, DenseNet-121, and MobileNetV2, are used to extract pretrained embeddings of facial images from the 5-Celebrity-Faces dataset that is augmented with a synthetic mask artificially placed on each facial image. These embeddings are passed to three machine learning classifiers, namely, categorical boosting, logistic regression, and linear discriminant analysis. The posterior class probabilities of individual classifiers for pairwise model combinations are fused by averaging to gain highly accurate classification results. The experimental results show that the soft decision fusion of MobileNetV2 and DenseNet-121 outperforms all other models with the maximum accuracy of 91.67% achieved using the linear discriminant analysis classifier.