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1. A LEARNING FRAMEWORK FOR AGE RANK ESTIMATION BASED ON FACE
IMAGES WITH SCATTERING TRANSFORM
ABSTRACT
This paper presents a cost-sensitive ordinalhyper planes ranking algorithm for human age
estimation basedon face images. The proposed approach exploits relative-orderinformation
among the age labels for rank prediction. In ourapproach, the age rank is obtained by aggregating
a series ofbinary classification results, where cost sensitivities among thelabels are introduced to
improve the aggregating performance.In addition, we give a theoretical analysis on designing
thecost of individual binary classifier so that the mistaking costcan be bounded by the total
misclassification costs. An efficientdescriptor, scattering transform, which scatters the Gabor
coefficientsand pooled with Gaussian smoothing in multiple layers,is evaluated for facial feature
extraction. We show that thisdescriptor is a generalization of conventional bioinspired
featuresand is more effective for face-based age inference. Experimentalresults demonstrate that
our method outperforms the state-ofthe-art age estimation approaches.