Towards a deep neural method based on freezing layers for in-the-wild facial emotion recognition

Abstract

Facial Expression Recognition (FER) is an active area of research in computer vision with a plethora of applications that have invested several techniques to improve recognition performance. We notice that most of these applications are oriented much more towards posed and environment-controlled emotions. However, FER in the wild remains an area deserving more attention. To address this issue, we investigate the use of deep learning-based methods, which have proven their effectiveness in several recent studies in FER. First, we challenge the studied methods within the context of in-the-wild using the Static Facial Expressions in-the-Wild (SFEW) benchmark dataset to assess their performance in real-world conditions. Then a method based on deep-learned features using effective CNN models, is proposed to handle the challenge involved in the comparative study. The suggested method performs a transfer learning based on freezing weights technique. Indeed, some shallow layers have been frozen, features in the deeper ones have been exploited to conceive the best configuration for the facial expression predictor model, and classification layers of models have been removed and replaced by our own SVM classifier. The proposed method achieved remarkable performance with the VGG19 and ResNet101 pre-trained models and outperformed other state-of-the-art deep learning methods for in-the-wild FER.