Utilising different representations of face images is known to be helpful in face recognition. In this study, the authors propose two fusion techniques that make use of multiple face image features by collaboratively training a similarity metric learner, based on Siamese neural networks. This training procedure takes two (or possibly more) features of two face images and outputs a similarity score that depicts whether the faces belong to the same person or not. The authors investigate two approaches of collaborative similarity metric learning (CoSiM), both of which are based on training Siamese neural networks jointly, as a means of early fusion. The experiments are employed on hand-crafted features such as scale-invariant feature transform (SIFT) and variants of the local binary pattern (LBP), on the YouTube Faces and the Labeled Faces in the Wild data sets. The authors provide theoretical and empirical comparisons of the proposed models against the related methods in the literature. It is shown that the proposed technique improves on the verification accuracy, compared to single feature-based baselines. By only utilising simple features like SIFT and LBP, the proposed techniques are shown to yield comparable results to the state of the art techniques, which depend on deep convolutional architectures or higher level features.

中文翻译：

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野外人脸识别的协作相似度度量学习

已知利用面部图像的不同表示有助于面部识别。在这项研究中， 作者 提出了两种融合技术，它们通过基于暹罗神经网络的协作训练相似性度量学习器来利用多个面部图像特征。该训练过程采用两个面部图像的两个（或可能更多）特征，并输出描述面部是否属于同一个人的相似度分数。 作者 研究了两种相似度协作学习（CoSiM）的方法，这两种方法都是基于联合训练暹罗神经网络作为早期融合的一种方法。实验是在YouTube面孔和Wild数据集中的YouTube面孔和带标签的面孔等手工制作的特征上进行的，例如比例不变特征变换（SIFT）和局部二进制模式（LBP）的变体。作者提供了所提出的模型与文献中相关方法的理论和经验比较。结果表明，与基于单个特征的基准相比，所提出的技术提高了验证准确性。仅通过利用诸如SIFT和LBP之类的简单功能，所提出的技术就可以产生与现有技术水平相当的结果，