Document Type : Research Paper


Computer Engineering Department, College of Engineering, University of Mosul, Mosul, Iraq


Kinship (family relationships) detection is important in many domains; it can be used in forensic investigations, adoption, biometric security, and more. It is particularly necessary in times of conflict and natural disasters, such as earthquakes, as it can help with reunions, and searches for missing persons. The most popular and very accurate means of establishing kinship is DNA analysis. Another method which is non-invasive is kinship estimate using facial images and computer vision accompanied with machine learning algorithms. Every component of the human body contains embeddedinformation that may be taken out and used for that person’s identification, verification, or classification. Finding characteristics that every family has in common is the foundation of kinship detection. This paper examines a novel approach of kinship detection using the hand geometry. Deep transfer learning using the ResNet50 model was used to extract geometrical features from hand images. A neural network classifier was designed and trained to predict kinship and assembled as a top layer for the ResNet model. The test accuracy of this novel methodology was 92.8% yielding to the hand has geometrical features that can be used to detect kinship, and that the proposed method is a possible potential way to identify kinship. We built our own hand image dataset that contains kinship ground truth metadata since there were no such datasets before. We called it “Mosul Kinship Hand (MKH) dataset”, which includes 648 photos of 81 people from 14 households (8 different hand images per person), and it was used in this research.


Main Subjects

  1. J. Qi, K. Xu, and X. Ding, “Approach to hand posture recognition based on hand shape features for human–robot interaction,” Complex Intell. Syst., vol. 8, no. 4, pp. 2825–2842, 2022.
  2. H. H. Mohammed, S. A. Baker, and A. S. Nori, “Biometric identity Authentication System Using Hand Geometry Measurements,” J. Phys. Conf. Ser., vol. 1804, no. 1, 2021.
  3. F. Bahmed, M. O. Mammar, and A. Ouamri, “A Multimodal Hand Recognition System Based on Finger Inner-Knuckle Print and Finger Geometry,” J. Appl. Secur. Res., vol. 14, no. 1, pp. 48–73, 2019.
  4. M. A. Rahman, R. H. M. A. Kabir, Z. Begum, M. A. Haque, and M. M. Haque, “A study of human recognition using inner joining lines of fingers,” Proceeding - 5th Int. Conf. Comput. Sci. Converg. Inf. Technol. ICCIT 2010, no. November, pp. 186–191, 2010.
  5. Z. Doˇ, “Two-Model-Based Online Hand Gesture Recognition from Skeleton Data,” vol. 4, no. Visigrapp, pp. 838–845, 2023.
  6. K. Prihodova and M. Hub, “Biometric Privacy through Hand Geometry- A Survey,” Proc. Int. Conf. Inf. Digit. Technol. 2019, IDT 2019, pp. 395–401, 2019.
  7. S. Angadi and S. Hatture, “Hand geometry based user identification using minimal edge connected hand image graph,” IET Comput. Vis., vol. 12, no. 5, pp. 744–752, 2018.
  8. R. Mukherjee, A. Bera, D. Bhattacharjee, and M. Nasipuri, “Human Gender Classification Based on Hand Images Using Deep Learning,”. International Symposium on Artificial Intelligence. ISAI 2022. communications in computer and information science, vol 1695. springer, cham.
  9. Y. Song, Z. Cai, and Z. L. Zhang, “Multi-touch Authentication Using Hand Geometry and Behavioral Information,” Proc. - IEEE Symp. Secur. Priv., pp. 357–372, 2017.
  10. M. Mudhafer Taher Al Mossawy and L. E. George, “A digital signature system based on hand geometry - Survey,” Wasit J. Comput. Math. Sci., vol. 1, no. 1, pp. 1–14, 2022.
  11. S. C. Jee, Y. S. Lee, J. H. Lee, S. Park, B. Jin, and M. H. Yun, “Anthropometric classification of human hand shapes in Korean population,” Proc. Hum. Factors Ergon. Soc., pp. 1199–1203, 2016.
  12. A. Bera, D. Bhattacharjee, and M. Nasipuri, “Person recognition using alternative hand geometry,” Int. J. Biom., vol. 6, no. 3, pp. 231–247, 2014.
  13. M. A. Abderrahmane, I. Guelzim, and A. A. Abdelouahad, “Human Age Prediction Based on Hand Image using Multiclass Classification,” 2020 Int. Conf. Data Anal. Bus. Ind. W. Towar. a Sustain. Econ. ICDABI 2020, 2020.
  14. E. Engineering, N. Dt, and N. Dt, “Linear Binary Pattern Based Biometric Recognition,” International Journal of Applied Engineering Research, Volume 10, Number 24 (2015) pp 45675-45683.
  15. N. L. Baisa, B. Williams, H. Rahmani, P. Angelov, and S. Black, “Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning,” 2022 11th Int. Conf. Image Process. Theory, Tools Appl. IPTA 2022, 2022.
  16. N. L. Baisa, “Joint Person Identity, Gender and Age Estimation from Hand Images using Deep Multi-Task Representation Learning,” ArXiv. /abs/2303.15263, pp. 1–6, 2023.
  17. O. Laiadi. “Kinship Verification Between Two People by Machine Learning, “ Université Polytechnique Hauts-de-France; Université Mohamed Khider (Biskra, Algérie), 2021. English. ⟨NNT : 2021UPHF0016.
  18. A. Goyal and T. Meenpal, “Kinship Verification From Facial Images Using Feature Descriptors, “ Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore..
  19. M. Afifi, “11K Hands: Gender recognition and biometric identification using a large dataset of hand images,” Multimed. Tools Appl., vol. 78, no. 15, pp. 20835–20854, 2019.
  20. I. H. Sarker, H. Alqahtani, F. Alsolami, A. I. Khan, and Y. B. Abushark, “Context pre ‑ modeling : an empirical analysis for classification based user ‑ centric context ‑ aware predictive modeling,” Journal of Big Data
  21. I. H. Sarker, “Machine Learning : Algorithms , Real ‑ World Applications and Research Directions,” SN Comput. Sci., vol. 2, no. 3, pp. 1–21, 2021.
  22. The PolyU Palmprint Database (version 3.0);, last seen at 30/12/2023