Recognizing human emotional state via SRC in Fractional Fourier Domain

Recognizing human emotional state is one of the most important component for efficient human-computer interaction (HCI). In this paper, a novel emotional state recognition method via SRC (classification based on sparse representation) in Fractional Fourier Domain (FRFD) is proposed. For a robust representation, it performs feature extraction by using the Fractional Fourier Transform (FRFT). And then Principal Component Analysis (PCA) and down-sample [1] are used to reduce the feature dimensions. In particular, the human emotional state recognition task is fitted into the SRC framework. Due to the FRFT and the excellent theory of SRC, the proposed algorithm gives better results when comparing with the state-of-art SRC human emotional state recognition method. Experiments conducted on publicly human emotional state database verify the accuracy and efficiency of our algorithm.