Soft biometric retrieval to describe and identify surveillance images

Abstract

Soft biometrics are human describable, distinguishing human characteristics. We present a baseline solution to the problem of identifying individuals solely from human descriptions, by automatically retrieving soft biometric labels from images. Probe images are then identified from a gallery of known soft biometric signatures, using their predicted labels. We investigate four labelling techniques and a number of challenging re-identification scenarios with this method. We also present a novel dataset, SoBiR, consisting of 8 camera viewpoints, 100 subjects and 4 forms of comprehensive human annotation to facilitate soft biometric retrieval. We report the increased retrieval accuracy of binary labels, the generalising capability of continuous measurements and the overall performance improvement of comparative annotations over categorical annotations.