Analysing comparative soft biometrics from crowdsourced annotations
| Autor: | John N. Carter, Mark S. Nixon, Daniel Martinho-Corbishley |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
021110 strategic
defence & security studies Information retrieval Biometrics Computer science Soft biometrics 0211 other engineering and technologies Subject (documents) 02 engineering and technology Set (abstract data type) Support vector machine Identification (information) Discriminative model Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Categorical variable Software |
| Zdroj: | IET Biometrics. 5:276-283 |
| ISSN: | 2047-4946 2047-4938 |
| DOI: | 10.1049/iet-bmt.2015.0118 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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