Cross-Generation Kinship Verification with Sparse Discriminative Metric
Kinship verification is a very important technique in many real-world applications, e.g., personal album organization, missing person investigation and forensic analysis. However, it is extremely difficult to verify a family pair with generation gap, e.g., father and son, since there exist both age...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 41(2019), 11 vom: 30. Nov., Seite 2783-2790 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2019
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Kinship verification is a very important technique in many real-world applications, e.g., personal album organization, missing person investigation and forensic analysis. However, it is extremely difficult to verify a family pair with generation gap, e.g., father and son, since there exist both age gap and identity variation. It is essential to well fight off such challenges to achieve promising kinship verification performance. To this end, we propose a towards-young cross-generation model for effective kinship verification by mitigating both age and identity divergences. Specifically, we explore a conditional generative model to bring in an intermediate domain to bridge each pair. Thus, we could extract more effective features through deep architectures with a newly-designed Sparse Discriminative Metric Loss (SDM-Loss), which is exploited to involve the positive and negative information. Experimental results on kinship benchmark demonstrate the superiority of our proposed model by comparing with the state-of-the-art kinship verification methods |
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| Beschreibung: | Date Completed 10.07.2020 Date Revised 10.07.2020 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2018.2861871 |