Kinship Verification in Childhood Images Using Vision Transformer
Facial Kinship Verification involves determining whether two face images belong to relatives, a task that is particularly challenging due to subtle differences in facial features and large intra-class variations. In recent years, deep learning models have shown great promise in addressing this probl...
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| Published in: | Procedia computer science Vol. 258; pp. 3105 - 3114 |
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| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
2025
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| Abstract | Facial Kinship Verification involves determining whether two face images belong to relatives, a task that is particularly challenging due to subtle differences in facial features and large intra-class variations. In recent years, deep learning models have shown great promise in addressing this problem. In this work, we propose a Vision Transformer (ViT) model for facial Kinship Verification, leveraging the proven effectiveness of Transformer architectures in Natural Language Processing. The Vision Transformer is trained end-to-end on two benchmark datasets: the large-scale Families in the Wild (FIW) dataset, consisting of thousands of face images with corresponding kinship labels, and the smaller KinFaceW-II dataset. Our model employs multiple attention mechanisms to capture complex relationships between facial features and produce a final kinship prediction. Experimental results demonstrate that our approach outperforms state-of-the-art methods, achieving an average accuracy of 92% on the FIW dataset and an F1 score of 0.85. The Euclidean distance metric further enhances the classification of kin and non-kin pairs. These findings confirm the effectiveness of Vision Transformer models for facial Kinship Verification and underscore their potential for future research in this domain. |
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| AbstractList | Facial Kinship Verification involves determining whether two face images belong to relatives, a task that is particularly challenging due to subtle differences in facial features and large intra-class variations. In recent years, deep learning models have shown great promise in addressing this problem. In this work, we propose a Vision Transformer (ViT) model for facial Kinship Verification, leveraging the proven effectiveness of Transformer architectures in Natural Language Processing. The Vision Transformer is trained end-to-end on two benchmark datasets: the large-scale Families in the Wild (FIW) dataset, consisting of thousands of face images with corresponding kinship labels, and the smaller KinFaceW-II dataset. Our model employs multiple attention mechanisms to capture complex relationships between facial features and produce a final kinship prediction. Experimental results demonstrate that our approach outperforms state-of-the-art methods, achieving an average accuracy of 92% on the FIW dataset and an F1 score of 0.85. The Euclidean distance metric further enhances the classification of kin and non-kin pairs. These findings confirm the effectiveness of Vision Transformer models for facial Kinship Verification and underscore their potential for future research in this domain. |
| Author | Meenpal, Toshanlal Majumdar, Saikat Oruganti, Madhu Tekchandani, Hitesh |
| Author_xml | – sequence: 1 givenname: Madhu surname: Oruganti fullname: Oruganti, Madhu email: madhu.oruganti@klh.edu.in organization: Asst. Prof., Department of AI & DS, Koneru Lakshmaiah Education Foundation, Aziz Nagar, Hyderabad, 500075, TG, India – sequence: 2 givenname: Toshanlal surname: Meenpal fullname: Meenpal, Toshanlal organization: Associate Prof., Department of ECE, National Institute of Technology, GE Road, Raipur, 492010, CG, India – sequence: 3 givenname: Saikat surname: Majumdar fullname: Majumdar, Saikat organization: Associate Prof., Department of ECE, National Institute of Technology, GE Road, Raipur, 492010, CG, India – sequence: 4 givenname: Hitesh surname: Tekchandani fullname: Tekchandani, Hitesh organization: Asst. Prof., Department of AI & DS, Koneru Lakshmaiah Education Foundation, Aziz Nagar, Hyderabad, 500075, TG, India |
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| Keywords | Accuracy F1 Score Vision Transformers Childhood Images Facial Kinship Verification Binary Classification Problem |
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| References | Madhu Oruganti, Toshanlal Meenpal, and Saikat Majumder, ”Selective variance based kinship verification in parent’s childhood and their children,” in *2021 National Conference on Communications (NCC)*, IEEE, 2021, pp. 1–6. Yellow Class Childhood (YCCH) contest,” Facebook page Accessed: 2022-04-05. Jiaxuan Zhu, Ming Shao, Chao Xia, Hong Pan, and Siyu Xia, ”Adversarial attacks on kinship verification using transformer,” in *2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)*, 2021, pp. 1-8, doi Aarti Goyal and Toshanlal Meenpal, ”Eccentricity based kinship verification from facial images in the wild,” *Pattern Analysis and Applications*, vol. 24, pp. 119–144, 2021, Springer. Oruganti, Meenpal, Saikat (bib6857) 2024; 118 Moumita Mukherjee and Toshanlal Meenpal, ”Kinship verification using compound local binary pattern and local feature discriminant analysis,” in *2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)*, IEEE, 2019, pp. 1–7. Joseph P. Robinson, Ming Shao, Handong Zhao, Yue Wu, Timothy Gillis, and Yun Fu, ”Recognizing families in the wild (RFIW) data challenge workshop in conjunction with ACM MM 2017,” in *Proceedings of the 2017 Workshop on Recognizing Families in the Wild*, 2017, pp. 5–12. . Aarti Goyal and Toshanlal Meenpal, ”Patch-based dual-tree complex wavelet transform for kinship recognition,” *IEEE Transactions on Image Processing*, vol. 30, pp. 191–206, 2020, IEEE. Meenpal Oruganti and Saikat Majumder, ”Stationary wavelet transform features for kinship verification in childhood images,” *Multimedia Tools and Applications*, pp. 1–26, 2023, Springer. Sheng Huang, Jingkai Lin, Luwen Huangfu, Yun Xing, Junlin Hu, and Daniel Dajun Zeng, ”Adaptively weighted k-tuple metric network for kinship verification,” *IEEE Transactions on Cybernetics*, vol. 53, no. 10, pp. 6173–6186, 2022, IEEE. Wanhua Li, Yingqiang Zhang, Kangchen Lv, Jiwen Lu, Jianjiang Feng, and Jie Zhou, ”Graph-based kinship reasoning network,” in *2020 IEEE International Conference on Multimedia and Expo (ICME)*, IEEE, 2020, pp. 1–6. Jiwen Lu, Xiuzhuang Zhou, Yap-Pen Tan, Yuanyuan Shang, and Jie Zhou, ”Neighborhood repulsed metric learning for kinship verification,” *IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol. 36, no. 2, pp. 331–345, 2013, IEEE. Yuting Zhang, Kihyuk Sohn, Ruben Villegas, Gang Pan, and Honglak Lee, ”Improving object detection with deep convolutional networks via bayesian optimization and structured prediction,” in *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, 2015, pp. 249–258. Zhong, Yaoyao, and Weihong Deng. ”Face transformer for recognition.” arXiv preprint arXiv:2103.14803 (2021). Huishan Wu, Jiawei Chen, Xiao Liu, and Junlin Hu, ”Component-based metric learning for fully automatic kinship verification,” *Journal of Visual Communication and Image Representation*, vol. 79, p. 103265, 2021, Elsevier. Madhu Oruganti, Toshanlal Meenpal, and Saikat Majumder, ”Easy pair selection method for kinship verification using fixed age group images,” *Image and Vision Computing*, p. 104727, 2023, Elsevier. 10.1016/j.procs.2025.04.568_bib6868 10.1016/j.procs.2025.04.568_bib6867 10.1016/j.procs.2025.04.568_bib6859 10.1016/j.procs.2025.04.568_bib6858 10.1016/j.procs.2025.04.568_bib6869 10.1016/j.procs.2025.04.568_bib6864 10.1016/j.procs.2025.04.568_bib6863 10.1016/j.procs.2025.04.568_bib6866 10.1016/j.procs.2025.04.568_bib6865 10.1016/j.procs.2025.04.568_bib6860 10.1016/j.procs.2025.04.568_bib6871 10.1016/j.procs.2025.04.568_bib6870 10.1016/j.procs.2025.04.568_bib6862 10.1016/j.procs.2025.04.568_bib6861 10.1016/j.procs.2025.04.568_bib6872 Oruganti (10.1016/j.procs.2025.04.568_bib6857) 2024; 118 |
| References_xml | – reference: Madhu Oruganti, Toshanlal Meenpal, and Saikat Majumder, ”Easy pair selection method for kinship verification using fixed age group images,” *Image and Vision Computing*, p. 104727, 2023, Elsevier. – reference: , Accessed: 2022-04-05. – reference: Madhu Oruganti, Toshanlal Meenpal, and Saikat Majumder, ”Selective variance based kinship verification in parent’s childhood and their children,” in *2021 National Conference on Communications (NCC)*, IEEE, 2021, pp. 1–6. – reference: Yuting Zhang, Kihyuk Sohn, Ruben Villegas, Gang Pan, and Honglak Lee, ”Improving object detection with deep convolutional networks via bayesian optimization and structured prediction,” in *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, 2015, pp. 249–258. – reference: Wanhua Li, Yingqiang Zhang, Kangchen Lv, Jiwen Lu, Jianjiang Feng, and Jie Zhou, ”Graph-based kinship reasoning network,” in *2020 IEEE International Conference on Multimedia and Expo (ICME)*, IEEE, 2020, pp. 1–6. – reference: Aarti Goyal and Toshanlal Meenpal, ”Eccentricity based kinship verification from facial images in the wild,” *Pattern Analysis and Applications*, vol. 24, pp. 119–144, 2021, Springer. – reference: Huishan Wu, Jiawei Chen, Xiao Liu, and Junlin Hu, ”Component-based metric learning for fully automatic kinship verification,” *Journal of Visual Communication and Image Representation*, vol. 79, p. 103265, 2021, Elsevier. – reference: Joseph P. Robinson, Ming Shao, Handong Zhao, Yue Wu, Timothy Gillis, and Yun Fu, ”Recognizing families in the wild (RFIW) data challenge workshop in conjunction with ACM MM 2017,” in *Proceedings of the 2017 Workshop on Recognizing Families in the Wild*, 2017, pp. 5–12. – reference: Sheng Huang, Jingkai Lin, Luwen Huangfu, Yun Xing, Junlin Hu, and Daniel Dajun Zeng, ”Adaptively weighted k-tuple metric network for kinship verification,” *IEEE Transactions on Cybernetics*, vol. 53, no. 10, pp. 6173–6186, 2022, IEEE. – reference: . – reference: Meenpal Oruganti and Saikat Majumder, ”Stationary wavelet transform features for kinship verification in childhood images,” *Multimedia Tools and Applications*, pp. 1–26, 2023, Springer. – reference: ”Yellow Class Childhood (YCCH) contest,” Facebook page, – volume: 118 start-page: 109375 year: 2024 ident: bib6857 article-title: ”Kinship verification in childhood images using curvelet transformed features." publication-title: Computers and Electrical Engineering – reference: Zhong, Yaoyao, and Weihong Deng. ”Face transformer for recognition.” arXiv preprint arXiv:2103.14803 (2021). – reference: Moumita Mukherjee and Toshanlal Meenpal, ”Kinship verification using compound local binary pattern and local feature discriminant analysis,” in *2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)*, IEEE, 2019, pp. 1–7. – reference: Jiwen Lu, Xiuzhuang Zhou, Yap-Pen Tan, Yuanyuan Shang, and Jie Zhou, ”Neighborhood repulsed metric learning for kinship verification,” *IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol. 36, no. 2, pp. 331–345, 2013, IEEE. – reference: Aarti Goyal and Toshanlal Meenpal, ”Patch-based dual-tree complex wavelet transform for kinship recognition,” *IEEE Transactions on Image Processing*, vol. 30, pp. 191–206, 2020, IEEE. – reference: Jiaxuan Zhu, Ming Shao, Chao Xia, Hong Pan, and Siyu Xia, ”Adversarial attacks on kinship verification using transformer,” in *2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)*, 2021, pp. 1-8, doi: – ident: 10.1016/j.procs.2025.04.568_bib6871 – ident: 10.1016/j.procs.2025.04.568_bib6867 doi: 10.1109/ICME46284.2020.9102823 – ident: 10.1016/j.procs.2025.04.568_bib6870 doi: 10.1007/s10044-020-00906-4 – volume: 118 start-page: 109375 year: 2024 ident: 10.1016/j.procs.2025.04.568_bib6857 article-title: ”Kinship verification in childhood images using curvelet transformed features." publication-title: Computers and Electrical Engineering doi: 10.1016/j.compeleceng.2024.109375 – ident: 10.1016/j.procs.2025.04.568_bib6872 – ident: 10.1016/j.procs.2025.04.568_bib6863 doi: 10.1109/NCC52529.2021.9530084 – ident: 10.1016/j.procs.2025.04.568_bib6859 doi: 10.1109/TPAMI.2013.134 – ident: 10.1016/j.procs.2025.04.568_bib6861 doi: 10.1145/3134421.3134424 – ident: 10.1016/j.procs.2025.04.568_bib6865 doi: 10.1109/TCYB.2022.3163707 – ident: 10.1016/j.procs.2025.04.568_bib6864 doi: 10.1016/j.imavis.2023.104727 – ident: 10.1016/j.procs.2025.04.568_bib6862 doi: 10.1016/j.jvcir.2021.103265 – ident: 10.1016/j.procs.2025.04.568_bib6866 doi: 10.1109/FG52635.2021.9667009 – ident: 10.1016/j.procs.2025.04.568_bib6858 doi: 10.1109/ICCCNT45670.2019.8944489 – ident: 10.1016/j.procs.2025.04.568_bib6868 doi: 10.1109/TIP.2020.3034027 – ident: 10.1016/j.procs.2025.04.568_bib6860 doi: 10.1109/CVPR.2015.7298621 – ident: 10.1016/j.procs.2025.04.568_bib6869 doi: 10.1007/s11042-023-16694-y |
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| SubjectTerms | Accuracy Binary Classification Problem Childhood Images F1 Score Facial Kinship Verification Vision Transformers |
| Title | Kinship Verification in Childhood Images Using Vision Transformer |
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