Island Loss for Learning Discriminative Features in Facial Expression Recognition

Understand

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.