High Boost 3-D Attention Network for Cross-Spectral Periocular Recognition
Recognition of individuals in a heterogeneous surveillance environment is a challenging problem as it involves matching images associated with different visual characteristics. These differences mainly arise due to the involvement of various wavelength ranges and sensing equipments. This letter pres...
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Published in | IEEE sensors letters Vol. 6; no. 9; pp. 1 - 4 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE
01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Recognition of individuals in a heterogeneous surveillance environment is a challenging problem as it involves matching images associated with different visual characteristics. These differences mainly arise due to the involvement of various wavelength ranges and sensing equipments. This letter presents a novel high boost 3-D attention mechanism embedded within a deep Siamese framework to address periocular recognition in heterogeneous wavelength domains. The proposed high boost attention module (HBAM) generates 3-D attended features by calculating the importance of each feature location. The HBAM works by enhancing significant components of the input feature map through a boost coefficient without any additional parameter overhead. We have created a new cross-spectral periocular dataset comprising 12 584 visible and near-infrared images from 200 classes by considering random pose (eye and head movement) and accessory (mask and eyeglass) variations. Experiments and ablation studies on five cross-spectral periocular datasets demonstrate efficacy of the proposed 3-D attention network and significance of its design choice. |
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ISSN: | 2475-1472 2475-1472 |
DOI: | 10.1109/LSENS.2022.3204710 |