The present invention relates to an apparatus for analyzing spatial information and dynamic information of video frames through a multilayer bi-long short term memory (LSTM) to predict a human depression level, which performs effective human depression level prediction in terms of performance. and a method thereof. According to an embodiment of the present invention, an apparatus for analyzing fine facial expression to predict a human depression level comprises: a data storage unit storing video data; a spatial information generation unit generating spatial information from video data; a volume local directional number (VLDN) feature map generation unit extracting three consecutive frames from the video data, generating a VLDN feature map for analyzing facial dynamics on the basis of the consecutive frames, and inputting the VLDN feature map into a deep convolutional neural network (CNN) model to generate dynamic information about facial movement; an information processing unit generating the spatial information and the dynamic information as output values through a temporal median pooling (TMP) method; and a prediction unit predicting a human depression level by using the output value on the basis of a recursive neural network.
BI-LSTM APPARATUS AND METHOD FOR PREDICTING HUMAN DEPRESSION LEVEL USING MULTI-LAYER BI-LSTM WITH SPATIAL AND DYNAMIC INFORMATION OF VIDEO FRAMES