Depression tendency recognition method based on multi-modal characteristics of limbs and micro-expressions

Abstract

The invention discloses a depression tendency recognition method based on multi-modal characteristics of limbs and micro-expressions. The method comprises the following steps: detecting human motion by means of a non-contact measurement sensor Kinect, and generating motion text description; capturing a face image frame by adopting a non-contact measurement sensor Kinect, performing Gabor wavelet and linear discriminant analysis on a face region of interest, performing feature extraction and dimensionality reduction, and then realizing face expression classification by adopting a three-layer neural network to generate expression text description; performing fusion through text description extracted by a fusion neural network with a self-organizing mapping layer and generating information with emotion features; and S4, using a Softmax classifier to classify the feature information generated in the S3 in emotion categories, wherein a classification result is used for evaluating whether the patient has a depression tendency or not. Static body movement and dynamic body movement are considered, and higher efficiency is achieved. Body movement is helpful for identifying the emotion of adepression patient.

Description

technical field [0001] The invention belongs to the field of emotion recognition, in particular to a depression tendency recognition method based on multimodal features of body and micro-expression. Background technique [0002] In order to detect patients prone to depression early, it is useful to monitor their mood. Human emotions can be recognized in various ways, such as electrocardiogram (ECG) (K. Takahashi, "Remarks on emotion recognition from multi-modal bio-potential signals", Proc.IEEE Int.Conf.Ind.Technol.(ICIT), vol .3, pp.1138-1143, Jun.2004.), electroencephalogram (EEG), speech, facial expressions, etc. Among various emotional signals, physiological signals are widely used in emotion recognition. In recent years, the movement of limbs has also become a new feature. [0003] There are two traditional detection methods, one is to measure the physiological indicators of objects by contact (J.Kim, and E.André, "Emotion recognition based on physiological changes i...

AI Technical Summary

Problems solved by technology

So non-intrusive methods are better, but in non-contact methods, objects can mask their emotions, so a single detection of facial expressions or detection of human limb movements will lead to lower recognition accuracy

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