Dimension emotion recognition method based on multi-scale time sequence modeling

Abstract

The invention discloses a dimension emotion recognition method based on multi-scale time sequence modeling. The method includes performing face detection and tracking on each frame image of a video sequence, and extracting face key points as first class face features; extracting gray values of pixels of a face region image, mouth region image and eye region image as second, third and fourth class face features; performing dimension emotion initial prediction according to the four classes of face features of the multiple frame image in a unit period t; performing time sequence and modality combination through a linear regression device according to the emotion initial prediction results of N unit periods t, and outputting an emotion prediction value of the video sequence. According to the method, time sequence modeling with different scales is performed on video sequence signals, and the precision prediction of each time sequence unit is implemented; the method is adaptive to emotion recognition of face signals of videos and has the advantages of fine real-time performance and greatly improved recognition precision.

Description

technical field [0001] The invention belongs to the field of video signal processing, and in particular relates to a method for dimensional emotion recognition based on multi-scale time series modeling, which improves the accuracy of continuous dimensional emotion recognition. Background technique [0002] In recent years, researchers at home and abroad have done a lot of research work on continuous dimension emotion recognition, and proposed many effective methods for emotion recognition. These methods can be divided into detection methods based on static classifiers and detection methods based on dynamic classifiers in terms of processing strategies. Detection methods based on static classifiers mostly use support vector machines (SVM), neural networks, Boosting, etc., and most of these classifiers are discriminative models. Due to its strong discrimination ability, it is widely used in the field of emotional state recognition, but this method ignores the fact that emotio...

AI Technical Summary

Problems solved by technology

However, HMM can only perform temporal modeling on a single time scale, and the scope of temporal modeling is limited, which cannot fully reflect the key role of emotional temporal information in emotion recognition.

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