文章基本信息

标题：Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition
本地全文：下载
作者：Ibrahim Omara ; Hongzhi Zhang ; Faqiang Wang 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2018
卷号：9
期号：9
页码：215-228
DOI：10.3390/info9090215
出版社：MDPI Publishing
摘要：The ear recognition task is known as predicting whether two ear images belong to the same person or not. More recently, most ear recognition methods have started based on deep learning features that can achieve a good accuracy, but it requires more resources in the training phase and suffer from time-consuming computational complexity. On the other hand, descriptor features and metric learning play a vital role and also provide excellent performance in many computer vision applications, such as face recognition and image classification. Therefore, in this paper, we adopt the descriptor features and present a novel metric learning method that is efficient in matching real-time for ear recognition system. This method is formulated as a pairwise constrained optimization problem. In each training cycle, this method selects the nearest similar and dissimilar neighbors of each sample to construct the pairwise constraints and then solves the optimization problem by the iterated Bregman projections. Experiments are conducted on Annotated Web Ears (AWE) database, West Pommeranian University of Technology (WPUT), the University of Science and Technology Beijing II (USTB II), and Mathematical Analysis of Images (AMI) databases.. The results show that the proposed approach can achieve promising recognition rates in ear recognition, and its training process is much more efficient than the other competing metric learning methods.
关键词：metric learning; ear recognition; pairwise constraint metric learning ; ear recognition ; pairwise constraint