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

A facial expression recognition and facial expression technology, applied in the field of image processing, can solve problems such as easy local optimal solutions, and achieve the effects of short recognition running time, improved speed and accuracy, and fast recognition speed

Inactive Publication Date: 2017-08-22
HANGZHOU DIANZI UNIV
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Problems solved by technology

Traditional neural network learning algorithms (such as BP algorithm) need to artificially set a l

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

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Embodiment Construction

[0054] Such as figure 1 As shown, first use the Adaboost algorithm to train the face region detection classifier, and combine several weak classifiers obtained from each training according to certain weights to obtain a strong classifier that can detect face regions. Then input the picture to be detected to the trained face detection classifier, and perform cropping, size pixel normalization and histogram equalization processing on the detected face area. Input the processed face expression picture into the trained ELM-AE feature extraction neural network, and the obtained hidden layer output matrix vector H is the texture feature vector of the whole face image. Finally, this feature vector is used as the input of the trained ELM expression recognition classifier, and the output of the corresponding expression category can be obtained.

[0055] The invention provides a fast human facial expression recognition method based on the ELM self-encoding algorithm. The ELM-AE algorit...

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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...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V40/174G06V40/172
Inventor 陆晗曹九稳朱心怡
Owner HANGZHOU DIANZI UNIV
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