TY - JOUR
T1 - Facial Kinship Verification
T2 - A Comprehensive Review and Outlook
AU - Wu, Xiaoting
AU - Feng, Xiaoyi
AU - Cao, Xiaochun
AU - Xu, Xin
AU - Hu, Dewen
AU - López, Miguel Bordallo
AU - Liu, Li
N1 - Funding Information:
The authors would like to thank the pioneering researchers in facial kinship verification and other related fields. The authors would also like to express their sincere appreciation to the associate editor and the anonymous reviewers for their comments and suggestions. 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.
PY - 2022/4/19
Y1 - 2022/4/19
N2 - 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.
AB - 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.
KW - Deep learning
KW - Facial analysis
KW - Feature extraction
KW - Kinship verification
KW - Metric learning
UR - http://www.scopus.com/inward/record.url?scp=85128411601&partnerID=8YFLogxK
U2 - 10.1007/s11263-022-01605-9
DO - 10.1007/s11263-022-01605-9
M3 - Article
C2 - 35465628
AN - SCOPUS:85128411601
SP - 1
EP - 32
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
SN - 0920-5691
ER -