Rapid facial expression recognition method based on ELM self-encoding algorithm

Abstract

The invention discloses a rapid facial expression recognition method based on an ELM self-encoding algorithm. The rapid facial expression recognition method comprises the steps of: step 1, training an Adaboost-based face region detection classifier and performing face detection; step 2, preprocessing a detected face region, including clipping, size normalization and histogram equalization processing; step 3, adopting an ELM-AE algorithm based on an auto-encoder and an extreme learning machine as a feature extraction algorithm, and performing feature extraction on a facial expression image after preprocessing; step 4, and constructing a facial expression classifier based on the extreme learning machine, inputting vectors of feature extraction into the facial expression classifier, and regarding an output result as a mood of a face. The rapid facial expression recognition method can extract primary information and perform dimensionality reduction more quickly and efficiently. When in facial expression recognition classification, the ELM just needs to adjust a parameter of a neuron, the recognition is short in operating time and high in accuracy rate, and the rapid facial expression recognition method is efficient and rapid in learning speed.

Description

technical field [0001] The invention belongs to the field of image processing, describes the whole process of emotion recognition of human facial expressions, and in particular relates to a fast human facial expression recognition method based on an ELM self-encoding algorithm. Background technique [0002] Emotion recognition of human facial expressions, that is, recognizing human faces in pictures or videos and performing further emotional analysis, has become a hot spot in the field of biological intelligence feature recognition in the past few decades. Emotion recognition is essentially to give computers the ability to "observe words and colors" and improve the current relatively rigid and immature human-computer interaction environment. [0003] The emotion analysis of facial expressions mainly includes the following aspects: face detection and positioning, image preprocessing, feature extraction of expressions, expression classification and emotion analysis. Face dete...

AI Technical Summary

Problems solved by technology

Traditional neural network learning algorithms (such as BP algorithm) need to artificially set a large number of network training parameters, so it is very easy to generate local optimal solutions

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