Facial Expression Recognition Based on Dual-Channel Fusion with Edge Features

In the era of artificial intelligence, accomplishing emotion recognition in human-computer interaction is a key work. Expressions contain plentiful information about human emotion. We found that the canny edge detector can significantly help improve facial expression recognition performance. A canny edge detector based dual-channel network using the OI-network and EI-Net is proposed, which does not add an additional redundant network layer and training. We discussed the fusion parameters of alpha and beta using ablation experiments. The method was verified in CK+, Fer2013, and RafDb datasets and achieved a good result.

Automated summary

Achieving emotion recognition in human-computer interaction is crucial in the era of artificial intelligence. We proposed a dual-channel network based on the Canny edge detector to improve facial expression recognition performance without adding redundant layers or training. Ablation experiments were conducted to discuss fusion parameters, and the method achieved good results in multiple datasets.