Face state recognition method and device based on deep learning

Abstract

The invention discloses a face state recognition method and a face state recognition device based on deep learning. The method comprises the steps: collecting a face image of a recognized person, recording the collection time, carrying out the processing of the image of the recognized person through a face recognition algorithm, and outputting a face recognition result. Inputting the face recognition result into a pre-trained deep neural network and a face feature point model for processing to obtain an expression recognition result and a fatigue recognition result; wherein the expression recognition result comprises an expression type and a predicted value thereof; taking the expression recognition result and the corresponding acquisition time as expression data, and sequentially recording the expression data into an expression database; and recording the fatigue data into the fatigue database according to time; acquiring a plurality of data from the expression and fatigue database and analyzing the data to obtain a state recognition result of the recognized person. The emotion and fatigue degree of the recognized person can be efficiently sensed and analyzed. The method and the device can be widely applied to the automation fields of fatigue driving detection, robots and the like.

Description

technical field [0001] The invention belongs to the technical field of image recognition processing, and relates to a face state recognition method and device based on deep learning. Background technique [0002] Emotion recognition refers to the study of an automatic, efficient, and accurate system to recognize the state of human facial expressions, and then understand people's emotional states, such as happiness, sadness, surprise, anger, etc., through facial expression information. This research has important application value in human-computer interaction, artificial intelligence, etc., and is one of the important topics in the fields of computer vision, pattern recognition, and affective computing. [0003] In the field of technology that requires human-computer interaction, especially robotics, it is usually necessary to be able to analyze human emotions in order to conduct effective human-computer interaction and bring sensory improvements to the user's interactive ex...

AI Technical Summary

Problems solved by technology

Most of the fatigue monitoring methods based on image processing only obtain the position below the driver's eyes, and do not further extract feature points for local feature information such as the human eyes.

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