Facial Kinship Verification: A Comprehensive Review and Outlook

Xiaoting Wu, Xiaoyi Feng, Xiaochun Cao, Xin Xu, Dewen Hu, Miguel Bordallo López, Li Liu (Corresponding Author)

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1-32
Number of pages32
JournalInternational Journal of Computer Vision
Early online date19 Apr 2022
DOIs
Publication statusE-pub ahead of print - 19 Apr 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Deep learning
  • Facial analysis
  • Feature extraction
  • Kinship verification
  • Metric learning

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