Recently, one paper on occluded face recognition is accepted by the journal IEEE TIP. The full name of IEEE TIP is IEEE Transactions on Image Processing, which is an international journal on computer vision and image processing with an impact factor of 10.856 announced in 2021. The paper information is as follow:
Mingjie He, Jie Zhang, Shiguang Shan, Xiao Liu, Zhongqin Wu, Xilin Chen, "Locality-aware Channel-wise Dropout for Occluded Face Recognition", IEEE Transactions on Image Processing (TIP), 2021. (Accepted)
Face recognition remains a challenging task
in unconstrained scenarios, especially when faces are partially occluded. To
improve the robustness against occlusion, augmenting the training images with
artificial occlusions has been proved as a useful approach. However, these
artificial occlusions are commonly generated by adding a black rectangle or
several object templates including sunglasses, scarfs and phones, which cannot
well simulate the realistic occlusions. In this paper, based on the argument
that the occlusion essentially damages a group of neurons, we propose a novel and
elegant occlusion-simulation method via dropping the activations of a group of
neurons in some elaborately selected channel. Specifically, we first employ a
spatial regularization to encourage each feature channel to respond to local
and different face regions. Then, the locality-aware channel-wise dropout (LCD)
is designed to simulate occlusions by dropping out a few feature channels. The
proposed LCD can encourage its succeeding layers to minimize the intra-class
feature variance caused by occlusions, thus leading to improved robustness
against occlusion. In addition, we design an auxiliary spatial attention module
by learning a channel-wise attention vector to reweight the feature channels,
which improves the contributions of non-occluded regions. Extensive experiments
on various benchmarks show that the proposed method outperforms
state-of-the-art methods with a remarkable improvement.
The main contributions of this paper are summarized as follows:
1) We propose a novel method to better simulate realistic occlusions by dropping a group of activations in intermediate features. It significantly improves the robustness to occlusions by encouraging the neural network to emphasize on learning discriminative features from the non-occluded face regions.
2) An auxiliary spatial attention module is designed to improve the contributions of non-occluded regions by adaptively reweighting the feature channels.
3) Our method significantly outperforms the state-of-the-art methods on IJB-C, LFW and MegaFace benchmarks, especially on the IJB-C dataset with large-scale real-occluded face images.
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