Island Loss for Learning Discriminative Features in Facial Expression Recognition
This work addresses facial expression recognition for applications like human-computer interaction, but it is incremental as it builds on existing CNN methods with a new loss function.
The paper tackles the problem of performance degradation in facial expression recognition under real-world variations by proposing a novel island loss to enhance feature discriminability, resulting in improved performance over baseline models and competitive results with state-of-the-art methods on four benchmark databases.
Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions. In this paper, a novel island loss is proposed to enhance the discriminative power of the deeply learned features. Specifically, the IL is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark expression databases have demonstrated that the CNN with the proposed island loss (IL-CNN) outperforms the baseline CNN models with either traditional softmax loss or the center loss and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.