DiscoStyle: Multi-level Logistic Ranking for Personalized Image Style Preference Inference

doi:10.1007/s11633-020-1244-1

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	DiscoStyle: Multi-level Logistic Ranking for Personalized Image Style Preference Inference
	Zhen-Wei He; Lei Zhang; Fang-Yi Liu
刊名	International Journal of Automation and Computing
	2020
卷号	17 期号:5 页码:637-651
关键词	Facial preference feature representation logistic regression face recommendation transfer learning.
ISSN号	1476-8186
DOI	10.1007/s11633-020-1244-1
英文摘要	Learning based on facial features for detection and recognition of people′s identities, emotions and image aesthetics has been widely explored in computer vision and biometrics. However, automatic discovery of users′ preferences to certain of faces (i.e., style), to the best of our knowledge, has never been studied, due to the subjective, implicative, and uncertain characteristic of psychological preference. Therefore, in this paper, we contribute to an answer to whether users′ psychological preference can be modeled and computed after observing several faces. To this end, we first propose an efficient approach for discovering the personality preference related facial features from only a very few anchors selected by each user, and make accurate predictions and recommendations for users. Specifically, we propose to discover the style of faces (DiscoStyle) for human′s psychological preference inference towards personalized face recommendation system/application. There are four merits of our DiscoStyle: 1) Transfer learning is exploited from identity related facial feature representation to personality preference related facial feature. 2) Appearance and geometric landmark feature are exploited for preference related feature augmentation. 3) A multi-level logistic ranking model with on-line negative sample selection is proposed for on-line modeling and score prediction, which reflects the users′ preference degree to gallery faces. 4) A large dataset with different facial styles for human′s psychological preference inference is developed for the first time. Experiments show that our proposed DiscoStyle can well achieve users′ preference reasoning and recommendation of preferred facial styles in different genders and races.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42264]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
推荐引用方式 GB/T 7714	Zhen-Wei He,Lei Zhang,Fang-Yi Liu. DiscoStyle: Multi-level Logistic Ranking for Personalized Image Style Preference Inference[J]. International Journal of Automation and Computing,2020,17(5):637-651.
APA	Zhen-Wei He,Lei Zhang,&Fang-Yi Liu.(2020).DiscoStyle: Multi-level Logistic Ranking for Personalized Image Style Preference Inference.International Journal of Automation and Computing,17(5),637-651.
MLA	Zhen-Wei He,et al."DiscoStyle: Multi-level Logistic Ranking for Personalized Image Style Preference Inference".International Journal of Automation and Computing 17.5(2020):637-651.