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.
|Title of host publication||ICMVA 2022 - 5th International Conference on Machine Vision and Applications|
|Publisher||Association for Computing Machinery|
|Number of pages||7|
|Publication status||Published - 2022 Feb 18|
|Event||5th International Conference on Machine Vision and Applications, ICMVA 2022 - Singapore, Singapore|
Duration: 2022 Feb 18 → 2022 Feb 20
|Name||ACM International Conference Proceeding Series|
|Conference||5th International Conference on Machine Vision and Applications, ICMVA 2022|
|Period||22/2/18 → 22/2/20|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (NO. NRF-2019R1A2C1003306).
© 2022 ACM.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition