Human face expression recognition method based on Curvelet transform and sparse learning

A technology of facial expression recognition and curvelet transform, applied in the field of facial expression recognition, to achieve good reconstruction ability, enhance discrimination ability, and improve the effect of facial expression recognition rate

Inactive Publication Date: 2017-07-25
HANGZHOU DIANZI UNIV
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  • Claims
  • Application Information

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This method has the advantages of simple and fast calculation and rich feature info

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  • Human face expression recognition method based on Curvelet transform and sparse learning
  • Human face expression recognition method based on Curvelet transform and sparse learning
  • Human face expression recognition method based on Curvelet transform and sparse learning

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[0028] The present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the described embodiments are only intended to facilitate the understanding of the present invention and do not have any limiting effect on it.

[0029] The embodiments of the present invention will be described in detail below with reference to the drawings.

[0030] figure 1 It is a flow chart of facial expression recognition required by the present invention, which mainly includes image acquisition, image preprocessing, expression feature extraction and classification recognition, showing the entire process of image input to output classification results.

[0031] figure 2 It is a Curvelet coefficient map based on the Curvelet feature extraction in the present invention.

[0032] The Curvelet transformation formula is defined as:

[0033]

[0034] Where f is the objective function, Is the curvelet basis function, j, l, k are the parameters of scale, d...

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Abstract

The invention discloses a human face expression recognition method based on Curvelet transform and sparse learning. The method comprises the following steps: 1, inputting a human face expression image, carrying out the preprocessing of the human face expression image, and cutting and obtaining an eye region and a mouth region from the human face expression image after processing; 2, extracting the human face expression features through Curvelet transform, carrying out the Curvelet transform and feature extraction of the human face expression image after preprocessing, the eye region and the mouth region, carrying out the serial fusion of the three features, and obtaining fusion features; 3, carrying out the classification recognition based on the sparse learning, and respectively employing SRC for classification and recognition of the human face Curvelet features and fusion features; or respectively employing FDDL for classification and recognition of the human face Curvelet features and fusion features. The Curvelet transform employed in the method is a multi-scale geometric analysis tool, and can extract the multi-scale and multi-direction features. Meanwhile, the method employs a local region fusion method, and enables the fusion features to be better in imaging representing capability and feature discrimination capability.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a facial expression recognition method. It belongs to the field of facial expression feature extraction and classification recognition. Background technique [0002] Facial expression recognition is a key component of affective computing and intelligent human-computer interaction, and it is also an important research direction in computer vision and biometrics. It mainly studies how to automatically, reliably and efficiently use the information conveyed by human facial expressions, and has a wide range of applications in medical health, traffic safety, public safety, intelligent robots, education, game entertainment and other fields. The exploration and research of facial expression recognition is not only conducive to promoting the natural harmony of human-computer interaction, but also conducive to promoting the development of artificial intelligence relate...

Claims

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V40/174G06V40/172
Inventor 付晓峰付克博
Owner HANGZHOU DIANZI UNIV
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