Open AccessAnalysing comparative soft biometrics from crowdsourced annotations
Author(s) -
MartinhoCorbishley Daniel,
Nixon Mark S.,
Carter John N.
Publication year - 2016
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2015.0118
Subject(s) - computer science , biometrics , discriminative model , categorical variable , artificial intelligence , set (abstract data type) , information retrieval , subject (documents) , natural language processing , machine learning , world wide web , programming language
Soft biometrics enable human description and identification from low‐quality surveillance footage. This study premises the design, collection and analysis of a novel crowdsourced dataset of comparative soft biometric body annotations, obtained from a richly diverse set of human annotators. The authors annotate 100 subject images to provide a coherent, in‐depth appraisal of the collected annotations and inferred relative labels. The dataset includes gender as a comparative trait and the authors find that comparative labels characteristically contain additional discriminative information over traditional categorical annotations. Using the authors’ pragmatic dataset, semantic recognition is performed by inferring relative biometric signatures using a RankSVM algorithm. This demonstrates a practical scenario, reproducing responses from a video surveillance operator searching for an individual. The approach can reliably return the correct match in the top 7% of results with ten comparisons, or top 13% of results using just five sets of subject comparisons.