In clinical diagnosis of mood disorder, depression is one of the most common psychiatric disorders. There are two major types of mood disorders: major depressive disorder (MDD) and bipolar disorder (BPD). A large portion of BPD are misdiagnosed as MDD in the diagnostic of mood disorders. Short-term detection which could be used in early detection and intervention is thus desirable. This study investigates microscopic facial expression changes for the subjects with MDD, BPD and control group (CG), when elicited by emotional video clips. This study uses eight basic orientations of motion vector (MV) to characterize the subtle changes in microscopic facial expression. Then, wavelet decomposition is applied to extract entropy and energy of different frequency bands. Next, an autoencoder neural network is adopted to extract the bottleneck features for dimensionality reduction. Finally, the long short term memory (LSTM) is employed for modeling the long-term variation among different mood disorders types. For evaluation of the proposed method, the elicited data from 36 subjects (12 for each of MDD, BPD and CG) were considered in the K-fold (K=12) cross validation experiments, and the performance for distinguishing among MDD, BPD and CG achieved 67.7% accuracy.