TY - GEN
T1 - Accounting for private taste
T2 - 12th International Conference on Knowledge and Smart Technology, KST 2020
AU - Tanaka, Takumi
AU - Mikuni, Jan
AU - Shimane, Daisuke
AU - Nakamura, Koyo
AU - Watanabe, Katsumi
N1 - Funding Information:
ACKNOWLEDGMENT We thank the authors of [9] for providing the valuable database. This research was partially supported by JSPS KAKENHI (17H06344)
Publisher Copyright:
© 2020 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - Attractiveness is an important facial attribute, which could largely influence individuals' impressions or social relationship. Lately, numerous studies have examined the morphological facial features driving attractiveness. Notably, most of the studies have postulated the 'ground-truth', universal standards of facial attractiveness. However, in fact, it is well-reported that there are inter-individual differences in attractiveness judgments. These individual differences may be derived from individual preferences for certain morphological features in the faces. Nevertheless, there has been no direct empirical study to investigate the variances in the evaluation of facial attractiveness. In this study, we examined the quantitative relationships between morphological facial features and the judgments of attractiveness ratings for the faces. We found that the variances in attractiveness ratings could be partly predicted by some traditional machine learning, and that the sharpness of face outlines and morphological features representing the smile expression could have impacts on the amounts of the variances.
AB - Attractiveness is an important facial attribute, which could largely influence individuals' impressions or social relationship. Lately, numerous studies have examined the morphological facial features driving attractiveness. Notably, most of the studies have postulated the 'ground-truth', universal standards of facial attractiveness. However, in fact, it is well-reported that there are inter-individual differences in attractiveness judgments. These individual differences may be derived from individual preferences for certain morphological features in the faces. Nevertheless, there has been no direct empirical study to investigate the variances in the evaluation of facial attractiveness. In this study, we examined the quantitative relationships between morphological facial features and the judgments of attractiveness ratings for the faces. We found that the variances in attractiveness ratings could be partly predicted by some traditional machine learning, and that the sharpness of face outlines and morphological features representing the smile expression could have impacts on the amounts of the variances.
KW - Facial Attractiveness
KW - Individual difference
KW - Private taste
KW - Sexual dimorphism
UR - http://www.scopus.com/inward/record.url?scp=85084035321&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084035321&partnerID=8YFLogxK
U2 - 10.1109/KST48564.2020.9059511
DO - 10.1109/KST48564.2020.9059511
M3 - Conference contribution
AN - SCOPUS:85084035321
T3 - KST 2020 - 2020 12th International Conference on Knowledge and Smart Technology
SP - 203
EP - 206
BT - KST 2020 - 2020 12th International Conference on Knowledge and Smart Technology
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 January 2020 through 1 February 2020
ER -