Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition
Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition
复制标题
具有动态生成的成对约束的度量学习用于耳朵识别
DOI：
10.3390/info9090215
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发表时间：
2018-03
期刊：
影响因子：
3.1
通讯作者：
Wangmeng Zuo
中科院分区：
文献类型：
--
作者：
Ibrahim Omara;Hongzhi Zhang;Faqiang Wang;Ahmed Hagag;Xiaoming Li;Wangmeng Zuo
耳部识别任务是指预测两张耳部图像是否属于同一个人。最近，大多数耳部识别方法开始基于深度学习特征，这些方法能够达到良好的准确性，但需要更多资源。
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.
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