Method for identifying multi-class facial expressions at high precision

Abstract

The invention relates to a method for identifying multi-class facial expressions at a high precision based on Haar-like features, which belongs to the technical field of computer science and graphic image process. Firstly, the high-accuracy face detection is achieved by using the Haar-like features and a series-wound face detection classifier; further, the feature selection is carried out on the high-dimension Haar-like feature by using the Ada Boost. MH algorithm; and finally, the expression classifier training is carried out by using the Random Forest algorithm to complete the expression identification. Compared with the prior art, the method can reduce training and identifying time while increasing the multi-class expression identification rate, and can implement the parallelization conveniently to increase the identification rate and meet the requirement of real-time processing and mobile computing. The method can identify the static image and the dynamic image at a high precision, is not only applicable to the desktop computer but also to the mobile computing platforms, such as cellphone, tablet personal computer and the like.

Description

technical field [0001] The invention relates to a multi-category facial expression high-precision recognition method based on Haar-like features, and belongs to the technical field of computer science and graphic image processing. Background technique [0002] Facial expression is an important way of human communication, and facial expression recognition (FER), as a technology in human-computer interaction, is getting more and more attention. People usually divide a variety of expressions into several basic categories, and then use classification methods to solve the recognition problem. For example, the Cohn-Kanade and JAFFE facial expression databases record 6 expressions of anger, disgust, fear, happiness, sadness, and surprise, and the CAS-PEAL-R1 facial expression database records smiles, frowns, surprises, opening mouths, and closing eyes 5 kind of expression. [0003] Facial expression recognition needs to solve two basic problems: 1. How to extract representative a...

AI Technical Summary

Problems solved by technology

The problem of this method in the application of expression recognition is that the extraction speed of signs is not fast enough, and the recognition accuracy of the discriminant model is insufficient

[0017] To sum up, for the application scenario of high-precision and high-speed recognition of multiple expressions, the existing feature extraction methods have shortcomings such as limited feature representation, insufficient accuracy and extraction speed; The accuracy is not ideal, the complexity is too high, the number of identifiable expression categories is limited, and the recognition speed is low.

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