Towards Face Representation Learning Conditioned on the Soft Biometrics

Jong Won Hwang, Leslie Ching Ow Tiong, Andrew Beng Jin Teoh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a method to leverage soft biometric as a means of conditioning biometrics for better face representation learning. By conditioning, we meant the soft biometric trait (age, gender, etc.) is used as an auxiliary biometric for training along with face modality while it is absent during the inference stage. We propose a two-stream deep neural network consisting of a multilayer perceptron network (MLP) and a convolutional neural network (CNN), which can learn a feature representation from soft biometric vectors and face images, respectively. The two-stream network can be optimized simultaneously and the information can be exploited from both biometrics. The learned conditioning soft biometric representation from the MLP serves as a center prototype of the feature learned from the face network, which is beneficial to contract the intra-class variation of the face feature representation. Due to the lacking of the face dataset that comes along with soft biometrics, we construct a database for evaluation purposes. Extensive experiments are performed on two face datasets that equip with soft biometrics and the results show the superiority of our method compared to the face modality alone.

Original languageEnglish
Title of host publicationICMVA 2022 - 5th International Conference on Machine Vision and Applications
PublisherAssociation for Computing Machinery
Pages1-7
Number of pages7
ISBN (Electronic)9781450395670
DOIs
Publication statusPublished - 2022 Feb 18
Event5th International Conference on Machine Vision and Applications, ICMVA 2022 - Singapore, Singapore
Duration: 2022 Feb 182022 Feb 20

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Machine Vision and Applications, ICMVA 2022
Country/TerritorySingapore
CitySingapore
Period22/2/1822/2/20

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (NO. NRF-2019R1A2C1003306).

Publisher Copyright:
© 2022 ACM.

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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