Toward to Reduction of Bias for Gender and Ethnicity from Face Images using Automated Skin Tone Classification
| dc.contributor.author | Molina, David | |
| dc.contributor.author | Causa, Leonardo | |
| dc.contributor.author | Tapia, Juan | |
| dc.contributor.editor | Brömme, Arslan | |
| dc.contributor.editor | Busch, Christoph | |
| dc.contributor.editor | Dantcheva, Antitza | |
| dc.contributor.editor | Raja, Kiran | |
| dc.contributor.editor | Rathgeb, Christian | |
| dc.contributor.editor | Uhl, Andreas | |
| dc.date.accessioned | 2020-09-16T08:25:47Z | |
| dc.date.available | 2020-09-16T08:25:47Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | This paper proposes and analyzes a new approach for reducing the bias in gender caused by skin tone from faces based on transfer learning with fine-tuning. The categorization of the ethnicity was developed based on an objective method instead of a subjective Fitzpatrick scale. A Kmeans method was used to categorize the color faces using clusters of RGB pixel values. Also, a new database was collected from the internet and will be available upon request. Our method outperforms the state of the art and reduces the gender classification bias using the skin-type categorization. The best results were achieved with VGGNET architecture with 96.71% accuracy and 3.29% error rate. | en |
| dc.identifier.isbn | 978-3-88579-700-5 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/34339 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-306 | |
| dc.subject | Gender classification | |
| dc.subject | Bias | |
| dc.subject | Skin-Detection | |
| dc.title | Toward to Reduction of Bias for Gender and Ethnicity from Face Images using Automated Skin Tone Classification | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 289 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 281 | |
| gi.conference.date | 16.-18. September 2020 | |
| gi.conference.location | International Digital Conference | |
| gi.conference.sessiontitle | Further Conference Contributions |
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