Delving Into Quaternion Wavelet Transformer for Facial Expression Recognition in the Wild

The Facial Expression Recognition (FER) technique has increasingly matured over time. However, recognizing facial expressions in wild environments poses great challenges in achieving promising performance. The main obstacles arise from various factors, such as illumination changes, head pose variations, and occlusions. To overcome interferences from external environments and improve recognition accuracy, we propose a novel Quaternion Wavelet TRansformer (QWTR) model for FER in the wild. Specifically, we present a Quaternion Value Transformer (QVT) network that combines quaternion multi-head attention with quaternion CNN to capture emotional cues from global and local perception. To preserve the color structure while enhancing image contrast and brightness, we introduce a Quaternion Histogram Equalization (QHE) representation to transform color images into quaternion matrices representation. After that, to alleviate the impact of head pose and occlusion together with feature redundancy, a Quaternion Wavelet Feature Selection (QWFS) scheme is designed to decompose quaternion features and select the most correlated signals. Extensive experiments have been conducted on four in-the-wild FER datasets and several specific FER benchmarks under various conditions. The qualitative and quantitative results demonstrate that QWTR outperforms other state-of-the-art methods in FER benchmarks, e.g., 68.37% vs. 66.31% accuracy on the AffectNet dataset.