Feature analysis of blind and visual signature data collection protocols based on the identification performance
| Autor: | Rehab Ibrahem, Meryem Erbilek |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
021110 strategic
defence & security studies Data collection Biometrics Computer science business.industry Feature extraction 0211 other engineering and technologies Pattern recognition Context (language use) 02 engineering and technology Signature (logic) Visualization Identification (information) Biometrics access control 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN). |
| DOI: | 10.1109/cicn.2017.8319370 |
| Popis: | In this paper, we analyse the differences and similarities of features in the context of blind and visual signing data collection protocols with respect to the signature biometrics identification performance. As a result of this performed experimental analysis, powerful features which maximises system accuracy while minimising the performance differential across different signature data collection protocols (visual and blind signing) is extensively tested and documented. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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