Deep Regression Forests for Age Estimation

CVPR 2018  ·  Wei Shen, Yilu Guo, Yan Wang, Kai Zhao, Bo wang, Alan Yuille ·

Age estimation from facial images is typically cast as a nonlinear regression problem. The main challenge of this problem is the facial feature space w.r.t. ages is heterogeneous, due to the large variation in facial appearance across different persons of the same age and the non-stationary property of aging patterns. In this paper, we propose Deep Regression Forests (DRFs), an end-to-end model, for age estimation. DRFs connect the split nodes to a fully connected layer of a convolutional neural network (CNN) and deal with heterogeneous data by jointly learning input-dependant data partitions at the split nodes and data abstractions at the leaf nodes. This joint learning follows an alternating strategy: First, by fixing the leaf nodes, the split nodes as well as the CNN parameters are optimized by Back-propagation; Then, by fixing the split nodes, the leaf nodes are optimized by iterating a step-size free and fast-converging update rule derived from Variational Bounding. We verify the proposed DRFs on three standard age estimation benchmarks and achieve state-of-the-art results on all of them.

PDF Abstract CVPR 2018 PDF CVPR 2018 Abstract

Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Age Estimation FGNET DRFs MAE 3.85 # 6
Age Estimation MORPH album2 (Caucasian) DRFs MAE 2.91 # 10

Methods


No methods listed for this paper. Add relevant methods here