High Boost 3-D Attention Network for Cross-Spectral Periocular Recognition

• Published in: IEEE sensors letters Vol. 6; no. 9; pp. 1 - 4
• Main Authors: Behera, Sushree S., Puhan, Niladri B.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; attention mechanism; convolutional neural network; cross-sensor; cross-spectral; Datasets; Face recognition; Feature extraction; Feature maps; Filtering; Head movement; Infrared imagery; Kernel; Mathematical analysis; periocular recognition; Recognition; Sensor signal processing; siamese; Surveillance; Tensors; Visualization
• ISSN: 2475-1472; 2475-1472
• DOI: 10.1109/LSENS.2022.3204710

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.