TL;DR
This paper introduces FROM, a deep neural network-based face recognition method that effectively handles occlusions by learning to identify and mask corrupted features, significantly improving accuracy on occluded face datasets.
Contribution
The paper proposes a novel end-to-end deep learning approach that learns to detect and mask occluded features, trained on a large-scale occluded face dataset, outperforming existing methods.
Findings
FROM improves face recognition accuracy under occlusions
It generalizes well to various face recognition benchmarks
The method outperforms existing occlusion-handling techniques
Abstract
With the recent advancement of deep convolutional neural networks, significant progress has been made in general face recognition. However, the state-of-the-art general face recognition models do not generalize well to occluded face images, which are exactly the common cases in real-world scenarios. The potential reasons are the absences of large-scale occluded face data for training and specific designs for tackling corrupted features brought by occlusions. This paper presents a novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network. Our approach, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks. In addition, we construct massive occluded face images to train FROM effectively and efficiently. FROM…
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