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Facial Age Estimation Using Robust Label Distribution
Ke Chen; Joni-Kristian Kämäräinen; Zhaoxiang Zhang
2016-10-15
Conference NameThe 2016 ACM Conference on Multimedia Conference
Source PublicationMM 2016
Conference DateOctober 15-19, 2016
Conference PlaceAmsterdam, The Netherlands
AbstractFacial 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.
KeywordFacial Age Estimation Robust Label Distribution Learning (Ldl)
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13251
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Ke Chen,Joni-Kristian Kämäräinen,Zhaoxiang Zhang. Facial Age Estimation Using Robust Label Distribution[C],2016.
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