BearWorks BearWorks
BearWorks BearWorks
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发表时间：
2021
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通讯作者：
A. Lumini;L. Nanni;S. Brahnam
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作者：
A. Lumini;L. Nanni;S. Brahnam
本文提出了一种新的人脸识别系统，该系统基于使用不同预处理技术的描述符集合，在野外工作良好。我们提出的方法的功能在两个数据集上得到了证明：FERET数据集和标记的野外面孔（LFW）数据集。在FERET数据集中，目标是识别，我们使用角距离。在LFW数据集中，目的是验证给定的匹配，我们使用支持向量机和相似性度量学习。我们提出的系统在这两个数据集上都表现良好，据我们所知，在FERET数据集的文献中，我们获得了最高的性能。特别值得注意的是，在没有使用额外的训练模式的情况下，在两个数据集上获得了这些良好的结果。我们的最佳集成方法的MATLAB源代码可在https://www.dei.unipd.it/node/ 2357免费获得。（cid:1） 2016。由爱思唯尔B.V.代表沙特国王大学制作和主持。这是
Presented in this paper is a novel system for face recognition that works well in the wild and that is based on ensembles of descriptors that utilize different preprocessing techniques. The power of our proposed approach is demonstrated on two datasets: the FERET dataset and the Labeled Faces in the Wild (LFW) dataset. In the FERET datasets, where the aim is identification, we use the angle distance. In the LFW dataset, where the aim is to verify a given match, we use the Support Vector Machine and Similarity Metric Learning. Our proposed system performs well on both datasets, obtaining, to the best of our knowledge, one of the highest performance rates published in the literature on the FERET datasets. Particularly noteworthy is the fact that these good results on both datasets are obtained without using additional training patterns. The MATLAB source of our best ensemble approach will be freely available at https://www.dei.unipd.it/node/ 2357. (cid:1) 2016 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is