Facial Age Estimation Using Robust Label Distribution

	Facial Age Estimation Using Robust Label Distribution
	Ke Chen; Joni-Kristian Kämäräinen; Zhaoxiang Zhang
	2016-10-15
会议日期	October 15-19, 2016
会议地点	Amsterdam, The Netherlands
关键词	Facial Age Estimation Robust Label Distribution Learning (Ldl)
英文摘要	Facial age estimation, to predict the persons' exact ages given facial images, usually encounters the data sparsity problem due to the difficulties in data annotation. To mitigate the suffering from sparse data, a recent label distribution learning (LDL) algorithm attempts to embed label correlation into a classification based framework. However, the conventional label distribution learning framework only considers correlations across the neighbouring variables (ages), which omits the intrinsic complexity of age classes during different ageing periods (age groups). In the light of this, we introduce a novel concept of robust label distribution for scalar-valued labels, which is designed to encode the age scalars into label distribution matrices, i.e. two-dimensional Gaussian distributions along age classes and age groups respectively. Overcoming the limitations of conventional hard group boundaries in age grouping and capturing intrinsic inter-group dependency, our framework achieves robust and competitive performance over the conventional algorithms on two popular benchmarks for human age estimation.
会议录	MM 2016
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/13251]
专题	自动化研究所_类脑智能研究中心
通讯作者	Zhaoxiang Zhang
推荐引用方式 GB/T 7714	Ke Chen,Joni-Kristian Kämäräinen,Zhaoxiang Zhang. Facial Age Estimation Using Robust Label Distribution[C]. 见:. Amsterdam, The Netherlands. October 15-19, 2016.