Architecture of a conventional SCNN. The network is trained by contrastive loss in the training stage, whereas a distance function is used to compute the similarity metric in the testing stage.

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The proposed ESCNN architecture, which consists of three parts: (a) Siamese, (b) extension, and (c) decision parts. The feature dimensions are denoted as h × w × d, and the bracketed numbers correspond to ESCNN-tiny, introduced in Section 4.3

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Visualization of the features learned by the ESCNN: (a) positive and (b) negative samples.

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Training strategy of the proposed network. The network is optimized by a combination of two loss functions: 1) contrastive loss for the Siamese part and 2) cross-entropy loss for all parts, including the extension and decision parts.

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Examples from the iLIDS–VID dataset.

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Some example results: (a) positive and (b) negative samples.

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ROC curves for the methods under consideration.

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