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Conference Paper: A Unified Framework for Masked And Mask-Free Face Recognition via Feature Rectification
Title | A Unified Framework for Masked And Mask-Free Face Recognition via Feature Rectification |
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Authors | |
Keywords | Computer vision and pattern recognition Machine learning Image and video processing |
Issue Date | 2022 |
Publisher | IEEE. |
Citation | 29th IEEE International Conference on Image Processing (IEEE ICIP), Bordeaux, France, 16-19 October, 2022 How to Cite? |
Abstract | Face recognition under ideal conditions is now considered a well-solved problem with advances in deep learning. Recognizing faces under occlusion, however, still remains a challenge. Existing techniques often fail to recognize faces with both the mouth and nose covered by a mask, which is now very common under the COVID-19 pandemic. Common approaches to tackle this problem include 1) discarding information from the masked regions during recognition and 2) restoring the masked regions before recognition. Very few works considered the consistency between features extracted from masked faces and from their mask-free counterparts. This resulted in models trained for recognizing masked faces often showing degraded performance on mask-free faces. In this paper, we propose a unified framework, named Face Feature Rectification Network (FFR-Net), for recognizing both masked and mask-free faces alike. We introduce rectification blocks to rectify features extracted by a state-of-the-art recognition model, in both spatial and channel dimensions, to minimize the distance between a masked face and its mask-free counterpart in the rectified feature space. Experiments show that our unified framework can learn a rectified feature space for recognizing both masked and mask-free faces effectively, achieving state-of-the-art results. |
Persistent Identifier | http://hdl.handle.net/10722/314917 |
DC Field | Value | Language |
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dc.contributor.author | Hao, S | - |
dc.contributor.author | Chen, C | - |
dc.contributor.author | Chen, Z | - |
dc.contributor.author | Wong, KKY | - |
dc.date.accessioned | 2022-08-05T09:36:55Z | - |
dc.date.available | 2022-08-05T09:36:55Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | 29th IEEE International Conference on Image Processing (IEEE ICIP), Bordeaux, France, 16-19 October, 2022 | - |
dc.identifier.uri | http://hdl.handle.net/10722/314917 | - |
dc.description.abstract | Face recognition under ideal conditions is now considered a well-solved problem with advances in deep learning. Recognizing faces under occlusion, however, still remains a challenge. Existing techniques often fail to recognize faces with both the mouth and nose covered by a mask, which is now very common under the COVID-19 pandemic. Common approaches to tackle this problem include 1) discarding information from the masked regions during recognition and 2) restoring the masked regions before recognition. Very few works considered the consistency between features extracted from masked faces and from their mask-free counterparts. This resulted in models trained for recognizing masked faces often showing degraded performance on mask-free faces. In this paper, we propose a unified framework, named Face Feature Rectification Network (FFR-Net), for recognizing both masked and mask-free faces alike. We introduce rectification blocks to rectify features extracted by a state-of-the-art recognition model, in both spatial and channel dimensions, to minimize the distance between a masked face and its mask-free counterpart in the rectified feature space. Experiments show that our unified framework can learn a rectified feature space for recognizing both masked and mask-free faces effectively, achieving state-of-the-art results. | - |
dc.language | eng | - |
dc.publisher | IEEE. | - |
dc.relation.ispartof | IEEE International Conference on Image Processing | - |
dc.rights | . Copyright © IEEE. | - |
dc.subject | Computer vision and pattern recognition | - |
dc.subject | Machine learning | - |
dc.subject | Image and video processing | - |
dc.title | A Unified Framework for Masked And Mask-Free Face Recognition via Feature Rectification | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Wong, KKY: kykwong@cs.hku.hk | - |
dc.identifier.authority | Wong, KKY=rp01393 | - |
dc.identifier.hkuros | 335243 | - |
dc.publisher.place | United States | - |