Open AccessFew‐shot palmprint recognition via graph neural networks
Author(s) -
Shao Huikai,
Zhong Dexing
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.1221
Subject(s) - computer science , convolutional neural network , artificial intelligence , pattern recognition (psychology) , benchmark (surveying) , artificial neural network , graph , image (mathematics) , geodesy , theoretical computer science , geography
At present, palmprint recognition based on deep learning has been more and more widely used in identity recognition due to its many advantages. However, these algorithms often require a large amount of labelled data for training. In fact, it is difficult and expensive to get enough data that meets the requirements. In this Letter, based on a small amount of labelled images, the authors proposed a method for few‐shot palmprint recognition using Graph Neural Networks (GNNs). The palmprint features extracted by the convolutional neural network are processed into nodes in the GNN. The edges in the GNN are used to represent similarities between image nodes. The parameters in the network are continuously optimised, and finally, the category to which each image belongs is obtained. Further, they adopted a mobile phone to create a palmprint database in an unconstrained way. Adequate experiments were performed on the benchmark database and the authors’ self‐built database. The experimental results show that their proposed GNN‐based few‐shot palmprint recognition can obtain state‐of‐the‐art performance, where the accuracy is over 99.90%.