Expression recognition method and system

Abstract

The invention relates to a facial expression recognition method and system. The method comprises the steps of identifying a face area of an image; extracting 68-point key points of each human face byutilizing Dlib to extract geometrical characteristics of the human face; wherein the geometrical characteristics comprise mouth height, mouth width, eye height, eyebrow height sum, eyebrow width sum,mouth minimum enclosing rectangle area, distance between an eyebrow and an eye head, eyebrow distance, lip bead and mouth corner height, and minimum enclosing rectangle area of two eyes; sending the geometrical characteristics of the human face into a naive Bayesian classifier, and determining an expression category; and during Bayesian decision, K neighbors are used for secondary learning to improve the classification effect. The scheme of the invention overcomes the defects of low classification efficiency, high calculation cost and influence on the effect of a classification algorithm caused by high dimension of existing face features.

Description

technical field [0001] The invention belongs to the field of artificial intelligence and psychology, and particularly relates to a face recognition technology. Background technique [0002] Facial expression is an important way for humans to convey emotions. Facial expression recognition technology can be widely used in human-computer interaction, computer vision, medical assistance, fatigue driving detection and other fields. [0003] There are three main steps in facial expression recognition: image preprocessing, feature extraction, and image classification. Feature extraction is the core technology in facial expression recognition. The existing facial expression feature extraction techniques mainly include Gabor filter, scale invariant feature transform (SIFT), histogram of gradient (HOG), linear discriminant analysis ( Traditional image feature extraction methods such as LDA) and local binary pattern (LBP); there are also popular deep learning feature extraction metho...

AI Technical Summary

Problems solved by technology

[0004] Although the feature extraction and classification based on deep learning can achieve better recognition results, the feature dimension extracted by deep learning is high, the amount of calculation is large, and the hardware requirements are high, which is difficult to meet in many places in real life. Deep learning requires hardware, and it is difficult to support facial expression technology

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