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Feature selection for subject ranking using Soft Biometric Queries

Feature selection for subject ranking using Soft Biometric Queries
Feature selection for subject ranking using Soft Biometric Queries
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.
barbuta, emil
dfb7fe56-d034-452d-b764-85d398cc84d7
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
barbuta, emil
dfb7fe56-d034-452d-b764-85d398cc84d7
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

barbuta, emil and Nixon, Mark (2018) Feature selection for subject ranking using Soft Biometric Queries. In 15th IEEE International Conference on Advanced Video and Signal-based Surveillance.

Record type: Conference or Workshop Item (Paper)

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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More information

Published date: November 2018
Venue - Dates: 15th IEEE International Conference on Advanced Video and Signal-based Surveillance: AVSS 2018, New Zealand, 2018-11-27 - 2018-11-30

Identifiers

Local EPrints ID: 426309
URI: http://eprints.soton.ac.uk/id/eprint/426309
PURE UUID: 20ec7711-7753-4ae6-8de9-d3029664be6c
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 22 Nov 2018 17:30
Last modified: 07 Oct 2020 06:52

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