Latent-PER: ICA-Latent Code Editing Framework for Portrait Emotion Recognition

Although real-image emotion recognition has been developed in several studies, an acceptable accuracy level has not been achieved in portrait drawings. This paper proposes a portrait emotion recognition framework based on independent component analysis (ICA) and latent codes to overcome the performance degradation problem in drawings. This framework employs latent code extracted through a generative adversarial network (GAN)-based encoder. It learns independently from factors that interfere with expression recognition, such as color, small occlusion, and various face angles. It is robust against environmental factors since it filters latent code by adding an emotion-relevant code extractor to extract only information related to facial expressions from the latent code. In addition, an image is generated by changing the latent code to the direction of the eigenvector for each emotion obtained through the ICA method. Since only the position of the latent code related to the facial expression is changed, there is little external change and the expression changes in the desired direction. This technique is helpful for qualitative and quantitative emotional recognition learning. The experimental results reveal that the proposed model performs better than the existing models, and the latent editing used in this process suggests a novel manipulation method through ICA. Moreover, the proposed framework can be applied for various portrait emotion applications from recognition to manipulation, such as automation of emotional subtitle production for the visually impaired, understanding the emotions of objects in famous classic artwork, and animation production assistance.