Periocular biometric covering the immediate vicinity of human eye is a synergistic alternative to face particularly when the face is masked or occluded. Most present work for periocular recognition in the wild are mainly convolutional neural networks learned based on cross-entropy loss. However, periocular images only capture the least salient face features, and thus suffering from severe intra-class compactness and inter-class dispersion issues for discriminative deep feature learning. Recently, label smoothing regularization (LSR) is discerned capable of diminishing the intra-class variation by minimizing the Kullback-Liebler divergence of a uniform distribution and a network prediction distribution. In this letter, we extend LSR to that of Generalized LSR (GLSR) by learning a pre-task network prediction, in place of the predefined uniform distribution. Extensive experiments on four periocular in the wild datasets disclose that the GSLR-trained networks prevail over the LSR-based counterpart and other most recent the state of the arts. This is supported by our empirical analyses that the embedding periocular features rendered by GLSR results in better class-wise cluster separation than the conventional LSR.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIP) (No. NRF- 2019R1A2C1003306).
Manuscript received July 1, 2020; revised July 30, 2020; accepted July 31, 2020. Date of publication August 5, 2020; date of current version September 2, 2020. This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIP) (No. NRF-2019R1A2C1003306). The associate author coordinating the review of this manuscript and approving it for publication was Xun Chen. (Corresponding author: Andrew Beng Jin Teoh.) The authors are with the School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul 120749, South Korea (e-mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org; email@example.com). Digital Object Identifier 10.1109/LSP.2020.3014472
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics