TY - GEN
T1 - A Cross-age Kinship Verification Scheme Using Face Age Transfer Model
AU - Kim, Hyeonwoo
AU - Kim, Hyungjoon
AU - Ko, Bumyeon
AU - Hwang, Eenjun
N1 - Funding Information:
ACKNOWLEDGMENT This work was partly supported by NRF (National Research Foundation of Korea) (No. NRF-2020R1F1A1074885) and NRF (No. NRF-2021R1A4A1031864) grant funded by the Korean Government.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - As the amount of face images collected from diverse devices such as smartphones, CCTVs and high-definition cameras is rapidly increasing, various face-based applications such as finding missing family members, social media analysis and genealogical research have attracted much attention. To effectively implement such applications, a technique called kinship verification can be used. Kinship verification determines the genetic relationship between people through facial image analysis. Recently, as convolutional neural network (CNN) shows good performance in image processing, many CCN-based kinship verification methods have been proposed. However, they suffered from problems such as insufficient labeled data to train deep learning models, and poor validation accuracy when the age difference between people was large. To alleviate these problems, in this paper, we propose a cross-age kinship verification scheme using a face age transfer model. To prove the effectiveness of the proposed scheme, we conducted several comparative experiments with other models, and we confirmed that the verification accuracy and f1 score of our proposed method improved.
AB - As the amount of face images collected from diverse devices such as smartphones, CCTVs and high-definition cameras is rapidly increasing, various face-based applications such as finding missing family members, social media analysis and genealogical research have attracted much attention. To effectively implement such applications, a technique called kinship verification can be used. Kinship verification determines the genetic relationship between people through facial image analysis. Recently, as convolutional neural network (CNN) shows good performance in image processing, many CCN-based kinship verification methods have been proposed. However, they suffered from problems such as insufficient labeled data to train deep learning models, and poor validation accuracy when the age difference between people was large. To alleviate these problems, in this paper, we propose a cross-age kinship verification scheme using a face age transfer model. To prove the effectiveness of the proposed scheme, we conducted several comparative experiments with other models, and we confirmed that the verification accuracy and f1 score of our proposed method improved.
KW - Convolutional neural network
KW - Face age transfer
KW - Facial image analysis
KW - Generative adversarial network
KW - Image processing
KW - Kinship verification
UR - http://www.scopus.com/inward/record.url?scp=85127599478&partnerID=8YFLogxK
U2 - 10.1109/BigComp54360.2022.00047
DO - 10.1109/BigComp54360.2022.00047
M3 - Conference contribution
AN - SCOPUS:85127599478
T3 - Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
SP - 206
EP - 210
BT - Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
A2 - Unger, Herwig
A2 - Kim, Young-Kuk
A2 - Hwang, Eenjun
A2 - Cho, Sung-Bae
A2 - Pareigis, Stephan
A2 - Kyandoghere, Kyamakya
A2 - Ha, Young-Guk
A2 - Kim, Jinho
A2 - Morishima, Atsuyuki
A2 - Wagner, Christian
A2 - Kwon, Hyuk-Yoon
A2 - Moon, Yang-Sae
A2 - Leung, Carson
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022
Y2 - 17 January 2022 through 20 January 2022
ER -