Kinship recognition from faces using deep learning with imbalanced data
Kinship verification from faces aims to determine whether two person share some family relationship based only on the visual facial patterns. This has attracted a significant interests among the scientific community due to its potential applications in social media mining and finding missing childre...
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| Other Authors: | , , , |
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2022
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| Online Access: | https://depot.sorbonne.ae/handle/20.500.12458/1321 |
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| _version_ | 1857415064326242305 |
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| author | Hadid, Abdenour |
| author2 | Othmani, Alice Han, Duqing Gao, Xin Ye, Runpeng |
| author2_role | author author author author |
| author_facet | Hadid, Abdenour Othmani, Alice Han, Duqing Gao, Xin Ye, Runpeng |
| author_role | author |
| dc.creator.none.fl_str_mv | Hadid, Abdenour Othmani, Alice Han, Duqing Gao, Xin Ye, Runpeng |
| dc.date.none.fl_str_mv | 2022-11-03T05:27:22Z 2022-11-03T05:27:22Z 2022 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 10.1007/s11042-022-14058-6 1380-7501 1573-7721 https://depot.sorbonne.ae/handle/20.500.12458/1321 10.1007/s11042-022-14058-6 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Multimedia Tools and Applications |
| dc.subject.none.fl_str_mv | Human-computer interaction Kinship recognition Deep visual learning Deep learning Biometrics |
| dc.title.none.fl_str_mv | Kinship recognition from faces using deep learning with imbalanced data |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | Kinship verification from faces aims to determine whether two person share some family relationship based only on the visual facial patterns. This has attracted a significant interests among the scientific community due to its potential applications in social media mining and finding missing children. In this work, We propose a novel pattern analysis technique for kinship verification based on a new deep learning-based approach. More specifically, given a pair of face images, we first use Resnet50 to extract deep features from each image. Then, feature distances between each pair of images are computed. Importantly, to overcome the problem of unbalanced data, One Hot Encoding for labels is utilised. The distances finally are fed to a deep neural networks to determine the kinship relation. Extensive experiments are conducted on FIW dataset containing 11 classes of kinship relationships. The experiments showed very promising results and pointed out the importance of balancing the training dataset. Moreover, our approach showed interesting ability of generalization. Results show that our approach performs better than all existing approaches on grandparents-grandchildren type of kinship. To support the principle of open and reproducible research, we are soon making our code publicly available to the research community: github.com/Steven-HDQ/Kinship-Recognition. |
| id | sorbonner_2beba486fc32b13e43eb134bfcd2b6db |
| identifier_str_mv | 10.1007/s11042-022-14058-6 1380-7501 1573-7721 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1321 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Kinship recognition from faces using deep learning with imbalanced dataHadid, AbdenourOthmani, AliceHan, DuqingGao, XinYe, RunpengHuman-computer interactionKinship recognitionDeep visual learningDeep learningBiometricsKinship verification from faces aims to determine whether two person share some family relationship based only on the visual facial patterns. This has attracted a significant interests among the scientific community due to its potential applications in social media mining and finding missing children. In this work, We propose a novel pattern analysis technique for kinship verification based on a new deep learning-based approach. More specifically, given a pair of face images, we first use Resnet50 to extract deep features from each image. Then, feature distances between each pair of images are computed. Importantly, to overcome the problem of unbalanced data, One Hot Encoding for labels is utilised. The distances finally are fed to a deep neural networks to determine the kinship relation. Extensive experiments are conducted on FIW dataset containing 11 classes of kinship relationships. The experiments showed very promising results and pointed out the importance of balancing the training dataset. Moreover, our approach showed interesting ability of generalization. Results show that our approach performs better than all existing approaches on grandparents-grandchildren type of kinship. To support the principle of open and reproducible research, we are soon making our code publicly available to the research community: github.com/Steven-HDQ/Kinship-Recognition.2022-11-03T05:27:22Z2022-11-03T05:27:22Z2022Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdf10.1007/s11042-022-14058-61380-75011573-7721https://depot.sorbonne.ae/handle/20.500.12458/132110.1007/s11042-022-14058-6enMultimedia Tools and Applicationsoai:depot.sorbonne.ae:20.500.12458/13212022-11-15T18:00:40Z |
| spellingShingle | Kinship recognition from faces using deep learning with imbalanced data Hadid, Abdenour Human-computer interaction Kinship recognition Deep visual learning Deep learning Biometrics |
| title | Kinship recognition from faces using deep learning with imbalanced data |
| title_full | Kinship recognition from faces using deep learning with imbalanced data |
| title_fullStr | Kinship recognition from faces using deep learning with imbalanced data |
| title_full_unstemmed | Kinship recognition from faces using deep learning with imbalanced data |
| title_short | Kinship recognition from faces using deep learning with imbalanced data |
| title_sort | Kinship recognition from faces using deep learning with imbalanced data |
| topic | Human-computer interaction Kinship recognition Deep visual learning Deep learning Biometrics |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1321 |