Retrieving relative soft biometrics for semantic identification

Martinho-Corbishley, Daniel, Nixon, Mark and Carter, John (2016) Retrieving relative soft biometrics for semantic identification. In, 2016 23rd International Conference on Pattern Recognition (ICPR). 23rd International Conference on Pattern Recognition (ICPR'16) (04/12/16 - 08/12/16) IEEE. (doi:10.1109/ICPR.2016.7900105).

Record type: Book Section

Abstract

Automatically describing pedestrians in surveillance footage is crucial to facilitate human accessible solutions for suspect identification. We aim to identify pedestrians based solely on human description, by automatically retrieving semantic attributes from surveillance images, alleviating exhaustive label annotation. This work unites a deep learning solution with relative soft biometric labels, to accurately retrieve more discriminative image attributes. We propose a Semantic Retrieval Convolutional Neural Network to investigate automatic retrieval of three soft biometric modalities, across a number of 'closed-world' and 'open-world' re-identification scenarios. Findings suggest that relative-continuous labels are more accurately predicted than absolute-binary and relative-binary labels, improving semantic identification in every scenario. Furthermore, we demonstrate a top rank-1 improvement of 23.2% and 26.3% over a traditional, baseline retrieval approach, in one-shot and multi-shot re-identification scenarios respectively.

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