Tied gender condition for facial expression recognition with deep random forest

Abstract

Facial expression recognition (FER) in an uncontrolled environment is difficult due to changes in occlusion, illumination, noise, and personal attributes. A deep learning enhanced gender conditional random forest (G_DRF) is proposed for FER in an uncontrolled environment. In order to reduce the influence of occlusion, illumination, low image resolution, etc., our method extracts robust facial features by deep multi-instance learning. Then a G_DRF is devised to address the facial personal attributes’ influence, such as gender variation by conditional RF. A large number of experiments were conducted on the public CK+, BU-3DEF, and LFW face databases. The experimental results showed that the proposed method had better performance and robustness than the state-of-the-art methods. The recognition rates of CK+, BU-3DEF, and LFW were 98.83%, 90%, 60.58%, respectively. Compared with other advanced deep learning methods that require a large number of training samples, the proposed method needs a small number of training samples and achieves better results.