Collaborative similarity metric learning for face recognition in the wild.

Utilising different representations of face images is known to be helpful in face recognition. Inthis study,theauthorspropose two fusion techniques that make use of multiple face image features bycollaboratively training a similarity metric learner, based on Siamese neuralnetworks. This training procedure takes two (or possibly more) features of twoface images and outputs a similarity score that depicts whether the faces belongto the same person or not.Theauthorsinvestigate two approaches of collaborative similarity metric learning (CoSiM),both of which are based on training Siamese neural networks jointly, as a meansof early fusion. The experiments are employed on hand‐crafted features such asscale‐invariant feature transform (SIFT) and variants of the local binarypattern (LBP), on the YouTube Faces and the Labeled Faces in the Wild data sets.The authors provide theoretical and empirical comparisons of the proposed modelsagainst the related methods in the literature. It is shown that the proposedtechnique improves on the verification accuracy, compared to singlefeature‐based baselines. By only utilising simple features like SIFT and LBP,the proposed techniques are shown to yield comparable results to the state ofthe art techniques, which depend on deep convolutional architectures or higherlevel features. [ABSTRACT FROM AUTHOR]

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