Fast and robust online-learning facial expression recognition and innate novelty detection capability of extreme learning algorithms

Facial Expression Recognition (FER) is a task usually framed as predicting an emotional state given a facial image. FER has received numerous attentions from both business and academia as it could serve as a crucial component in various applications, e.g., automatic evaluation of customer satisfaction, sign language recognition, and human–computer interaction. Despite active research on the subject, facial expression recognition remains greatly challenging due to the diversity of individual expressions, shapes, and sizes of face, eyes, mouth, and other facial features, as well as orientation, alignment, and lighting. In this paper, we aim to improve the fast and robust online-learning FER with unseen data identification. We compare two widely used feature extraction methods for FER, namely Curvelet Transform (CT) and Local Curvelet Transform (LCT). Furthermore, we explore factors underlying several online extreme learning approaches for unseen data identification. Our experimental results demonstrate that (1) CT is suitable for a cleaner and well-prepared dataset, while LCT seems to work well on a dataset with diverse quality and on level of consistency. (2) The Identity Structural Tolerance Sequential Circular Extreme Learning Machine outperforms other Extreme Learning algorithms employed in FER. (3) LC can provide unseen identification capability to Extreme Learning algorithms. These findings emphasize the common underlying foundation between the extreme learning approach and other traditional learning approaches.