A multi-angle facial expression recognition algorithm combined with dual-channel WGAN-GP is suggested to address the concerns of poor performance of standard algorithms for multi-angle facial expression identification and bad quality of frontal face pictures generated under deflection angles. Traditional models only use profile features to recognize the multi-angle facial expression, which leads to low recognition accuracy due to small differences in characteristics. As a result, the generative adversarial network is used to frontalize the face first, removing the impact of the pose angle. To stabilize the training of the model and improve the quality of face generation, WGAN-GP is used as the baseline and improved into a dual-channel structure, which fuses the facial features and the global features of the face for frontalization. Finally, the lightweight network MobileNetV3 is built to detect the produced frontal facial expression photos, ensuring classification accuracy while drastically reducing parameter calculation. The experimental results demonstrate that the proposed method can well generate the frontal facial expression images at any angle and enhance the recognition rate of multi-angle facial expressions.