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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],2016.
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