Feature selection for subject ranking using Soft Biometric Queries

Abstract

This paper presents a feature selection model that aims to identify subjects from low-resolution surveillance images based on a soft biometric description query. The process is divided into three main stages. In the first stage, semantic
segmentation is performed on the subjects, classifying and localising different parts of their bodies / accessories. The second stage extracts information from the segmentations and maps each subject to a vector in a soft biometric feature
space. Last but not least, the purpose of the final stage is to find a good weighting on the features extracted in the previous step, based on the intuition that some of them are more important, more accurate or have a higher variance. It is assumed that the matching process might benefit considerably from a set of good weights. Analysis on the IEEE AVSS Challenge dataset shows encouraging performance for segmentation and subject matching with the correct subject reliably matched just outside the top ten on the training set, and just outside top 10% on the recently released test set.

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Identifiers

ORCID for Mark Nixon: orcid.org/0000-0002-9174-5934

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