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Facial expression recognition method based on feature block weighting

A facial expression and recognition method technology, applied in the field of human facial expression recognition, can solve the problems of different contribution rates of facial expression recognition, and achieve the effect of improving recognition accuracy and improving commonality

Active Publication Date: 2017-09-15
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] In view of the defects in the prior art, the purpose of the present invention is to propose a facial expression recognition method based on feature block weighting to solve the problem that different forms of features and features in different regions of the face have different contribution rates to facial expression recognition

Method used

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  • Facial expression recognition method based on feature block weighting
  • Facial expression recognition method based on feature block weighting
  • Facial expression recognition method based on feature block weighting

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Embodiment 1

[0025] see figure 1 , the facial expression recognition method based on feature block weighting is characterized in that the operation steps are as follows: 1) extract the Gabor texture feature and geometric feature of the expression picture; 2) use the PCA algorithm to reduce the feature dimension for the extracted Gabor texture feature, and extract The geometric features are aligned in blocks, and the geometric features are divided into three feature blocks of the mouth, left eye, and right eye, and the Procrustes Analysis method is used to align each geometric feature; 3) Gabor texture features after PCA dimensionality reduction and The three geometric feature blocks after Procrustes Analysis are fused to form a fused feature; 4) The fused feature is input to the Bp neural network weighted by the feature block, and the neural network is trained to find the appropriate weight coefficient of each layer.

Embodiment 2

[0027] This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0028] The Gabor texture and the geometric feature of the described extraction expression picture are: adopt the Gabor filter to extract the Gabor texture feature of the expression image, adopt the Face++ function library to extract the geometric feature of the expression image.

[0029] The alignment of the geometric feature blocks is: divide the geometric features into left eye geometric feature blocks, right eye geometric feature blocks and mouth geometric feature blocks, and then use Procrustes Analysis to perform alignment processing on each feature block.

[0030] The feature fusion is: arranging and combining the Gabor feature and each geometric feature block in the form of a column vector.

[0031] The Bp neural network method of described characteristic block weighting is: before the input layer of neural network, increased weight layer, weight layer comprises four...

Embodiment 3

[0033] Such as figure 1 , using the Gabor filter to extract the Gabor features of facial expressions. Since the Gabor features have many feature dimensions, in high-dimensional feature representations, these features are usually linearly correlated and contain more useless or less useful variables, so , using the PCA algorithm to perform feature selection on the extracted Gabor features. Use the Face++ function library to extract the facial geometric features of the expression image. Due to the difference in the structure and size of the face, the position and size of the eyes and mouth are different, the extracted facial expression geometric features are divided into mouth, left eye, and right eye geometric features. block, and then perform Procrustes Analysis on each feature block of facial features separately. The extracted Gabor features are fused with the features after the block alignment of the geometric features to form the fused features. The fusion method is as foll...

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Abstract

The present invention relates to a facial expression recognition method based on feature block weighting. The method is as follows: 1) extracting Gabor texture features and geometric features of an expression image; 2) carrying out feature dimensionality reduction on the extracted Gabor texture features by using the PCA algorithm, carrying out partitioning alignment on the extracted geometric features, dividing the geometric features into three feature blocks of the mouth, the left eye and the right eye, and carrying out alignment on each geometric feature respectively by using the Procrustes Analysis method; 3) fusing the Gabor texture features after being subjected to the PCA dimensionality reduction and the three feature blocks after being subjected to the Procrustes Analysis to form fusion features; and 4) inputting the fusion features in to a Bp neural network of the feature block weighting, training the neural network, and seeking the appropriate weight coefficient of each layer. According to the method provided by the present invention, the commonality of the geometric features of the expression is improved, and problems that different feature forms and the different area features of the face has different contribution rates to the expression recognition are solved.

Description

technical field [0001] The invention relates to a facial expression recognition technology, in particular to a method of weighting each feature block based on the feature block and weighting a Bp (backpropagation) neural network. Background technique [0002] The biggest problem facing the research on facial expression recognition is how to improve the accuracy of facial expression recognition. Due to the influence of different regions, races, face sizes, skin colors, cultures, etc., the current facial expression recognition methods do not have good performance. Versatility, not robust to different people. [0003] The feature extraction of facial expressions is very critical to the recognition of facial expressions. Different feature extraction methods represent features from different perspectives. However, different features have different contribution rates to the recognition of facial expressions. In order to distinguish the feature importance of different features and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/084G06V40/175G06N3/045
Inventor 许烁张二东江渊广张鹏王阳周可璞
Owner SHANGHAI UNIV
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