towards nir vis masked face recognition CORD-Papers-2021-10-25 (Version 1)

Title: Towards NIR-VIS Masked Face Recognition
Abstract: Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition, which aims to match a pair of face images captured from two different modalities Existing deep learning based methods have made remarkable progress in NIR-VIS face recognition, while it encounters certain newly-emerged difficulties during the pandemic of COVID-19, since people are supposed to wear facial masks to cut off the spread of the virus We define this task as NIR-VIS masked face recognition, and find it problematic with the masked face in the NIR probe image First, the lack of masked face data is a challenging issue for the network training Second, most of the facial parts (cheeks, mouth, nose) are fully occluded by the mask, which leads to a large amount of loss of information Third, the domain gap still exists in the remaining facial parts In such scenario, the existing methods suffer from significant performance degradation caused by the above issues In this paper, we aim to address the challenge of NIR-VIS masked face recognition from the perspectives of training data and training method Specifically, we propose a novel heterogeneous training method to maximize the mutual information shared by the face representation of two domains with the help of semi-siamese networks In addition, a 3D face reconstruction based approach is employed to synthesize masked face from the existing NIR image Resorting to these practices, our solution provides the domain-invariant face representation which is also robust to the mask occlusion Extensive experiments on three NIR-VIS face datasets demonstrate the effectiveness and cross-dataset-generalization capacity of our method IEEE
Published: 2021
Journal: IEEE Signal Processing Letters
Author Name: Du, H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/du_h
Author Name: Shi, H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/shi_h
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Zeng, D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zeng_d
Author Name: Mei, T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mei_t
license: unk
license_url: [unknown license]
source_x: WHO
source_x_url: https://www.who.int/
who_covidence_id: #covidwho-1197074
has_full_text: FALSE
G_ID: towards_nir_vis_masked_face_recognition
S2 ID: 233231647