Investigating Deep Neural Forests for Facial Expression Recognition

摘要

Facial Expression Recognition (FER) usually involves learning intermediate representations from high-dimensional, potentially noisy images, that can be adapted to unknown face morphologies. Moreover, it needs to fulfil the real-time constraint to be useful, e.g. for consumer robotics or healthcare systems. To tackle this issue, Random Forests (RFs) are convenient predictors, as the hard decisions at each node allow non-linear subdivisions of the space with a very fast evaluation runtime. However RFs are non-differenciable predictors, making it impossible to back-propagate the error down to upstream feature extraction layers (e.g. CNN). In this paper, we investigate the adaptation of deep Neural Forests (NFs) to FER. The latter allows to combine the best of the two worlds, i.e. a differentiable classification model that can be trained with backpropagation and stochastic gradient descent but has the runtime of a RF. We show that NFs provides competitive results for FER as compared to state-of-the-art approaches when trained upon various combinations of geometric/CNN-based features.