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Compact Models for Periocular Verification Through Knowledge Distillation

: Boutros, Fadi; Damer, Naser; Fang, Meiling; Raja, Kiran; Kirchbuchner, Florian; Kuijper, Arjan

Brömme, Arslan (Ed.) ; Gesellschaft für Informatik -GI-, Bonn:
BIOSIG 2020, 19th International Conference of the Biometrics Special Interest Group. Proceedings : 16.-18.09.2020, Fully Virtual Conference
Bonn: GI, 2020 (GI-Edition - Lecture Notes in Informatics (LNI). Proceedings P-306)
ISBN: 978-3-88579-700-5
Gesellschaft für Informatik, Special Interest Group on Biometrics and Electronic Signatures (BIOSIG International Conference) <19, 2020, Online>
Fraunhofer IGD ()
Lead Topic: Smart City; Lead Topic: Visual Computing as a Service; Research Line: Computer vision (CV); Research Line: Human computer interaction (HCI); biometric; knowledge processing; deep learning; machine learning; CRISP; ATHENE

Despite the wide use of deep neural network for periocular verification, achieving smaller deep learning models with high performance that can be deployed on low computational powered devices remains a challenge. In term of computation cost, we present in this paper a lightweight deep learning model with only 1.1m of trainable parameters, DenseNet-20, based on DenseNet architecture. Further, we present an approach to enhance the verification performance of DenseNet-20 via knowledge distillation. With the experiments on VISPI dataset captured with two different smartphones, iPhone and Nokia, we show that introducing knowledge distillation to DenseNet-20 training phase outperforms the same model trained without knowledge distillation where the Equal Error Rate (EER) reduces from 8.36% to 4.56% EER on iPhone data, from 5.33% to 4.64% EER on Nokia data, and from 20.98% to 15.54% EER on cross-smartphone data.