A media processing method, the method being applied to a media processing device, the method comprising: obtaining a to-be-processed video, the to-be-processed video comprising an object with a to-be-recognized identity; generating a first gait energy diagram according to the to-be-processed video; obtaining a second gait energy diagram, the second gait energy diagram being generated according to a video comprising an object with a known identity; and performing first gait recognition on the first gait energy diagram and the second gait energy diagram by using a deep neural network, the first gait recognition comprising: extracting identity information of the first gait energy diagram and identity information of the second gait energy diagram, and determining a fused gait feature vector based on a gait feature vector of the first gait energy diagram and a gait feature vector of the second gait energy diagram, the identity information of the first gait energy diagram comprising an identifier of the first gait energy diagram and the gait feature vector of the first gait energy diagram, and the identity information of the second gait energy diagram comprising an identifier of the second gait energy diagram and the gait feature vector of the second gait energy diagram; and calculating a similarity between the first gait energy diagram and the second gait energy diagram according to at least the fused gait feature vector, wherein the calculating a similarity between the first gait energy diagram and the second gait energy diagram according to at least the fused gait feature vector comprises: calculating a first similarity between the first gait energy diagram and the second gait energy diagram according to the fused gait feature vector; calculating a second similarity between the first gait energy diagram and the second gait energy diagram according to the identity information of the first gait energy diagram and the identity information of the second gait energy diagram; and calculating the similarity between the first gait energy diagram and the second gait energy diagram as a weighted sum on the first similarity and the second similarity. obtaining a to-be-processed video, the to-be-processed video comprising an object with a to-be-recognized identity; generating a first gait energy diagram according to the to-be-processed video; obtaining a second gait energy diagram, the second gait energy diagram being generated according to a video comprising an object with a known identity; and performing first gait recognition on the first gait energy diagram and the second gait energy diagram by using a deep neural network, the first gait recognition comprising: extracting identity information of the first gait energy diagram and identity information of the second gait energy diagram, and determining a fused gait feature vector based on a gait feature vector of the first gait energy diagram and a gait feature vector of the second gait energy diagram, the identity information of the first gait energy diagram comprising an identifier of the first gait energy diagram and the gait feature vector of the first gait energy diagram, and the identity information of the second gait energy diagram comprising an identifier of the second gait energy diagram and the gait feature vector of the second gait energy diagram; and calculating a similarity between the first gait energy diagram and the second gait energy diagram according to at least the fused gait feature vector, extracting identity information of the first gait energy diagram and identity information of the second gait energy diagram, and determining a fused gait feature vector based on a gait feature vector of the first gait energy diagram and a gait feature vector of the second gait energy diagram, the identity information of the first gait energy diagram comprising an identifier of the first gait energy diagram and the gait feature vector of the first gait energy diagram, and the identity information of the second gait energy diagram comprising an identifier of the second gait energy diagram and the gait feature vector of the second gait energy diagram; and calculating a similarity between the first gait energy diagram and the second gait energy diagram according to at least the fused gait feature vector, wherein the calculating a similarity between the first gait energy diagram and the second gait energy diagram according to at least the fused gait feature vector comprises: calculating a first similarity between the first gait energy diagram and the second gait energy diagram according to the fused gait feature vector; calculating a second similarity between the first gait energy diagram and the second gait energy diagram according to the identity information of the first gait energy diagram and the identity information of the second gait energy diagram; and calculating the similarity between the first gait energy diagram and the second gait energy diagram as a weighted sum on the first similarity and the second similarity. calculating a first similarity between the first gait energy diagram and the second gait energy diagram according to the fused gait feature vector; calculating a second similarity between the first gait energy diagram and the second gait energy diagram according to the identity information of the first gait energy diagram and the identity information of the second gait energy diagram; and calculating the similarity between the first gait energy diagram and the second gait energy diagram as a weighted sum on the first similarity and the second similarity.