  Convolutional Neural Networks (ConvNets) have successfully contributed to
improve the accuracy of regression-based methods for computer vision tasks such
as human pose estimation, landmark localization, and object detection. The
network optimization has been usually performed with L2 loss and without
considering the impact of outliers on the training process, where an outlier in
this context is defined by a sample estimation that lies at an abnormal
distance from the other training sample estimations in the objective space. In
this work, we propose a regression model with ConvNets that achieves robustness
to such outliers by minimizing Tukey's biweight function, an M-estimator robust
to outliers, as the loss function for the ConvNet. In addition to the robust
loss, we introduce a coarse-to-fine model, which processes input images of
progressively higher resolutions for improving the accuracy of the regressed
values. In our experiments, we demonstrate faster convergence and better
generalization of our robust loss function for the tasks of human pose
estimation and age estimation from face images. We also show that the
combination of the robust loss function with the coarse-to-fine model produces
comparable or better results than current state-of-the-art approaches in four
publicly available human pose estimation datasets.
