Recognizing Human Emotional State Based on the Phase Information of the Two Dimensional Fractional Fourier Transform

Over the last decade, automatic facial expression analysis has become an active research area which finds potential applications in fields such as more engaging human-computer interaction, multimedia information analysis and retrieval, biometrics for security and surveillance, entertainment and e-health. In this paper, we explore a new class of visual features for recognizing human emotion states from. It performs feature extraction by using the method of two dimensional fractional Fourier transform (2D-FrFT). As a generalization of Fourier transform, the 2D-FrFT contains the time-frequency information of the signal at the same time, and is a new and powerful tool for time-frequency analysis. In particular, features are extracted from the phase parts of the 2DFrFT, and used to train the Fisher's Linear Discriminant Analysis (FLDA) classifiers for human emotion recognition. Preliminary experiments show that the proposed 2D-FrFT features yield promising results in visual human emotion recognition. More importantly, the 2D-FrFT and the one dimensional fractional Fourier transform provide a natural, versatile and powerful platform for general audiovisual signal processing tasks.