Facial Age Estimation Using Robust Label Distribution

CASIA OpenIR > 模式识别实验室

	Facial Age Estimation Using Robust Label Distribution
	Ke Chen; Joni-Kristian Kämäräinen; Zhaoxiang Zhang
	2016-10-15
会议名称	The 2016 ACM Conference on Multimedia Conference
会议录名称	MM 2016
会议日期	October 15-19, 2016
会议地点	Amsterdam, The Netherlands
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.
关键词	Facial Age Estimation Robust Label Distribution Learning (Ldl)
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13251
专题	模式识别实验室
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],2016.