Deep Domain Adaptation for Facial Expression Analysis

Nikolai Kalischek, Patrick Thiam, Peter Bellmann, Friedhelm Schwenker

Abstract: Deep learning has attracted a lot of attention in various fields over the past few years, including facial expression recognition. However, applying deep learning techniques to facial expression recognition is not straightforward. There are several drawbacks for successful deep expression recognition systems. Besides the lack of sufficient training data, facial expressions convey various inter-personal morphological and character differences. Therefore, an expression recognition network often suffers from overfitting and missing generalizability. However, multiple learning techniques, generally known as domain adaptation, have been proposed to address the lack of sufficient data and missing variance. Consequently, facial expression recognition may profit from domain adaptation. In this paper, we evaluate the applicability of deep domain adaptation for facial expression recognition. We describe two domain adaptation frameworks, one for single frame facial expression analysis and one for sequence-based facial expression analysis based on the Self-Ensembling method defined in [1]. The former is evaluated on the CK+ dataset [2], [3], the latter on the SenseEmotion database [4] of the University of Ulm. Our results indicate that domain adaptation is mostly applicable for person-specific facial expression recognition.