Facial age estimation by deep residual decision making
This work addresses age estimation from facial images, an incremental improvement for computer vision applications.
The paper tackled facial age estimation by incorporating residual learning into deep neural decision forests, achieving state-of-the-art accuracy on three public benchmarks with reduced memory and computation requirements.
Residual representation learning simplifies the optimization problem of learning complex functions and has been widely used by traditional convolutional neural networks. However, it has not been applied to deep neural decision forest (NDF). In this paper we incorporate residual learning into NDF and the resulting model achieves state-of-the-art level accuracy on three public age estimation benchmarks while requiring less memory and computation. We further employ gradient-based technique to visualize the decision-making process of NDF and understand how it is influenced by facial image inputs. The code and pre-trained models will be available at https://github.com/Nicholasli1995/VisualizingNDF.