Improving head pose estimation using two-stage ensembles with top-k regression

摘要

Conventional head pose estimation methods are regarded as a classification or regression paradigm, individually. The accuracy of classification-based approaches is limited to pose quantized interval and regression-based methods are fragile due to extremely large pose in non-ideal conditions. On the contrary to these methods, this paper introduces a novel head pose estimation method using two-stage ensembles with average top-k regression. The first stage is a binned classification subtask with the optimal pose partition. The second stage achieves average top-k regression based on the former prediction. Then we combine the two subtasks by considering the task-dependent weights instead of setting coefficients by grid search. We conduct several experiments to analyze the optimal pose partition for classification part and to validate the average top-k loss for regression part. Furthermore, we report the performance of proposed method on AFW, AFLW2000 and BIWI datasets and results show rather competitive performance in head pose prediction.