Object emotion analysis method and apparatus and electronic device

An electronic device, comprising a processor and a memory, wherein the memory stores machine executable instructions executable by the processor, and the processor is configured to execute the machine executable instructions to implement an object emotion analysis method, the object emotion analysis method comprising:

• acquiring multimedia data associated with a target object, and extracting a static facial feature and a dynamic feature of the target object from the multimedia data, wherein the dynamic feature comprises at least one of an expression change feature, a sound feature or a language content feature of the target object;
• and
• inputting the static facial feature and the dynamic feature into an object emotion analysis model, wherein the object emotion analysis model is pre-trained, fusing the static facial feature and the dynamic feature by the object emotion analysis model to obtain a fusion feature, and outputting an emotion analysis result of the target object based on the fusion feature, wherein the object emotion analysis model comprises a dynamic feature processing module, and the dynamic feature processing module is configured to input the dynamic feature of a corresponding type;
• and
• wherein fusing the static facial feature and the dynamic feature by the object emotion analysis model to obtain the fusion feature comprises:
• inputting, for the dynamic feature processing module, the dynamic feature corresponding to the dynamic feature processing module and the static facial feature of the target object into the dynamic feature processing module;
• and
• fusing the static facial feature and the dynamic feature by an attention network in the dynamic feature processing module to obtain the fusion feature, wherein the fusion feature comprises a common key feature for characterizing an emotion, between the static facial feature and the dynamic feature, wherein the method further comprises:
• inputting the dynamic feature into a time sequence model, and outputting a context feature of the dynamic feature, wherein the context feature is configured for characterizing a variation trend of the dynamic feature with time;
• and
• adding time sequence information into the context feature, and determining the context feature in which the time sequence information is added, as the dynamic feature to be input into the dynamic feature processing module, wherein the time sequence information is configured for indicating time sequence positions of features in the context feature.

A non-transitory machine-readable storage medium, wherein the non-transitory machine-readable storage medium stores machine executable instructions, and when invoked and executed by a processor, the machine executable instructions cause the processor to implement an object emotion analysis method, the object emotion analysis method comprising:

• acquiring multimedia data associated with a target object, and extracting a static facial feature and a dynamic feature of the target object from the multimedia data, wherein the dynamic feature comprises at least one of an expression change feature, a sound feature or a language content feature of the target object;
• and
• inputting the static facial feature and the dynamic feature into an object emotion analysis model, wherein the object emotion analysis model is pre-trained, fusing the static facial feature and the dynamic feature by the object emotion analysis model to obtain a fusion feature, and outputting an emotion analysis result of the target object based on the fusion feature, wherein the object emotion analysis model comprises a dynamic feature processing module, and the dynamic feature processing module is configured to input the dynamic feature of a corresponding type;
• and
• wherein fusing the static facial feature and the dynamic feature by the object emotion analysis model to obtain the fusion feature comprises:
• inputting, for the dynamic feature processing module, the dynamic feature corresponding to the dynamic feature processing module and the static facial feature of the target object into the dynamic feature processing module;
• and
• fusing the static facial feature and the dynamic feature by an attention network in the dynamic feature processing module to obtain the fusion feature, wherein the fusion feature comprises a common key feature for characterizing an emotion, between the static facial feature and the dynamic feature, wherein the method further comprises:
• inputting the dynamic feature into a time sequence model, and outputting a context feature of the dynamic feature, wherein the context feature is configured for characterizing a variation trend of the dynamic feature with time;
• and
• adding time sequence information into the context feature, and determining the context feature in which the time sequence information is added, as the dynamic feature to be input into the dynamic feature processing module, wherein the time sequence information is configured for indicating time sequence positions of features in the context feature.