is paper presents a method of depression recognition based on direct measurement of a ective disorder. Firstly, visual emotional stimuli are used to obtain eye movement behavior signals and physiological signals directly related to mood. en, in order to eliminate noise and redundant information and obtain better classi cation features, statistical methods (FDR corrected ttest) and principal component analysis (PCA) are used to select features of eye movement behavior and physiological signals. Finally, based on feature extraction, we use kernel extreme learning machine (KELM) to recognize depression based on PCA features. e results show that, on the one hand, the classi cation performance based on the fusion features of eye movement behavior and physiological signals is better than using a single behavior feature and a single physiological feature; on the other hand, compared with previous methods, the proposed method for depression recognition achieves better classi cation results.is study is of great value for the establishment of an automatic depression diagnosis system for clinical use.