|Table of Contents|

Near-infrared and Visible-light Image Heterogeneous Face RecognitionBased on Adversarial Domain Adaptation Learning(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2020年04期
Page:
95-103
Research Field:
·智慧应急信息技术·
Publishing date:

Info

Title:
Near-infrared and Visible-light Image Heterogeneous Face RecognitionBased on Adversarial Domain Adaptation Learning
Author(s):
Zhang Shuai; Xie Zhihua; Niu Jieyi; Li Yi
Key Laboratory of Optic-Electronic and Communication,Jiangxi Sciences and Technology Normal University,Nanchang 330031,China
Keywords:
heterogeneous face recognition; unsupervised learning; adversarial learning; domain adaptation
PACS:
TP39
DOI:
10.3969/j.issn.1001-4616.2020.04.014
Abstract:
The paper uses innovatively the unsupervised learning method to reduce the modal difference between bimodal images from the perspective of adaptive domain discrimination. A new near-infrared and visible-light heterogeneous face recognition method is proposed based on adversarial domain adaption learning. Firstly,we use cross-entropy and central loss function to jointly pre-train a full-convolution network,which gives the network a strong discriminating ability and provides the prior knowledge to another network. Then,another structurally consistent full convolutional network is trained by the adversarial loss,so that the data distribution of features extracted by the two network is consistent which narrowing the gap between the modalities. Finally,using the prior knowledge provided by the previous network,we can output the posterior probability of another modal images. Experimental results show that the proposed method can achieve the good performance,without requiring of the label information of near-infrared face image or large-scale training dataset.

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Last Update: 2020-11-15