학술논문
Building an Active Palmprint Recognition System
Document Type
Conference
Author
Source
2019 IEEE International Conference on Image Processing (ICIP) Image Processing (ICIP), 2019 IEEE International Conference onhttps://idams.ieee.org/idams/custom/properties/properties.jsp#. :1685-1689 Sep, 2019
Subject
Language
ISSN
2381-8549
Abstract
Palmprint recognition allows accurate identity verification to build a security system. Recently, researchers introduce deep learning to this area that largely improves the recognition accuracy. However, as a supervised approach, its performance relies on availability of data and labels for every registered identity. For large-scale security systems, after image acquisition, we need to check the whole dataset and manually assign labels through comparison, which is a time-consuming task. Besides, labelling some redundant training samples contributes little to the recognition result. In this paper, we introduce an active learning framework to select the best sample set for label assignment. We regard the active learning as a binary classification task and attempt to make the labeled and unlabeled set indistinguishable. Experiments on different datasets demonstrate our model can reduce the annotation cost while achieving comparable recognition performance.