Depression recognition method and device based on face video and storage medium

Abstract

The invention provides a depression recognition method and device based on a face video and a storage medium. The depression recognition method comprises the following steps: extracting a pre-acquired target spatial-temporal feature of each to-be-recognized face sub-video through a pre-trained target depression recognition model; outputting a depression degree recognition score of each to-be-recognized face sub-video through a target depression recognition model, and determining a thermodynamic video of each to-be-recognized face sub-video through a thermodynamic diagram generator; determining a first depression recognition result of the target person based on the depression degree recognition score of each to-be-recognized face sub-video; determining a second depression recognition result of the target person based on the thermodynamic video; and determining a final depression recognition result of the target person based on the first depression recognition result and the second depression recognition result. In this way, the final depression recognition result of the target person is determined based on the depression degree recognition score obtained based on the spatial-temporal features of the to-be-recognized face sub-video and the thermal video, and the accuracy of the depression recognition result can be effectively improved.

Description

technical field [0001] The present application relates to the technical field of computer vision, in particular to a facial video-based depression recognition method, device and storage medium. Background technique [0002] Depression has become a major mental disease that endangers human life and health. This mental disease affects people of all ages and causes serious health problems for patients. Nowadays, the diagnostic methods for depression mainly focus on the interview process under the guidance of professional physicians. Experienced physicians can obtain diagnostic clues from the abnormal expressions of patients. [0003] Inspired by this, there have been related studies that use expression signals to complete the automatic detection and recognition of depression. However, this kind of research is mostly focused on the recognition of static facial images, and cannot be combined with dynamic facial change information for depression recognition. The result is less ac...

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Problems solved by technology

[0003] Inspired by this, there have been related studies that use expression signals to complete the automatic detection and recognition of depression. However, this kind of research is mostly focused on the recognition of static facial images, and cannot be combined with dynamic facial change information for depression recognition, which leads to the recognition of depression. The result is less accurate

Moreover, the output depression recognition result is usually depression or the depression score corresponding to the static picture, but this output result cannot intuitively show the facial expression state of the depression patient, so it cannot better assist the doctor to make further judgments

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