University of Oulu

Wu, X., Feng, X., Cao, X. et al. Facial Kinship Verification: A Comprehensive Review and Outlook. Int J Comput Vis 130, 1494–1525 (2022).

Facial kinship verification : a comprehensive review and outlook

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Author: Wu, Xiaoting1,2; Feng, Xiaoyi2; Cao, Xiaochun3;
Organizations: 1University of Oulu, Oulu, Finland
2Northwestern Polytechnical University, Xi’an, China
3Sun Yat-sen University, Guangzhou, China
4National University of Defense Technology, Changsha, China
5VTT Technical Research Centre of Finland, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.8 MB)
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Language: English
Published: Springer Nature, 2022
Publish Date: 2022-08-31


The goal of Facial Kinship Verification (FKV) is to automatically determine whether two individuals have a kin relationship or not from their given facial images or videos. It is an emerging and challenging problem that has attracted increasing attention due to its practical applications. Over the past decade, significant progress has been achieved in this new field. Handcrafted features and deep learning techniques have been widely studied in FKV. The goal of this paper is to conduct a comprehensive review of the problem of FKV. We cover different aspects of the research, including problem definition, challenges, applications, benchmark datasets, a taxonomy of existing methods, and state-of-the-art performance. In retrospect of what has been achieved so far, we identify gaps in current research and discuss potential future research directions.

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Series: International journal of computer vision
ISSN: 0920-5691
ISSN-E: 1573-1405
ISSN-L: 0920-5691
Volume: 130
Issue: 6
Pages: 1494 - 1525
DOI: 10.1007/s11263-022-01605-9
Type of Publication: A2 Review article in a scientific journal
Field of Science: 113 Computer and information sciences
Funding: This work was partially supported by the National Key Research and Development Program of China No. 2021YFB3100800, the Academy of Finland under Grant 331883, the National Natural Science Foundation of China under Grant 61872379, and Key Research and Development Program of Shaanxi under 2020GY-050.
Academy of Finland Grant Number: 331883
Detailed Information: 331883 (Academy of Finland Funding decision)
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