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Expression recognition method based on BN parameter transfer learning

A technology of expression recognition and transfer learning, applied in character and pattern recognition, acquisition/recognition of facial features, instruments, etc., can solve problems such as large subjectivity and unfavorable BN parameter estimation

Pending Publication Date: 2020-10-23
SHAANXI UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

However, the acquisition of expert experience often has greater subjectivity, which is not conducive to the estimation of BN parameters.

Method used

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  • Expression recognition method based on BN parameter transfer learning
  • Expression recognition method based on BN parameter transfer learning
  • Expression recognition method based on BN parameter transfer learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0105] The source of the data set in this example is two parts:

[0106] (1) CK database (Source: Swati NigamRajiv SinghEmailauthorA.K.Misra.Efficient facial expression recognition using histogram oforiented gradients in wavelet domain,Multimedia Tools and Applications,Vol.77(21)(2018)28725-28747.) Available in the part The subjects were 97 undergraduates in an introductory psychology course. Their ages range from 18 to 30 years old. 65 percent are women, 15 percent are African American, and 3 percent are Asian or Latino. Including six basic expressions, it is currently a relatively common facial expression data set. Such as Image 6 shown.

[0107] (2) FER2013 dataset

[0108] The FER2013 facial expression dataset (source: Hong-Wei Ng, Viet Dung Nguyen, Vassilios Vonikakis, Stefan Winkler, Deep learning for emotion recognition on small datasets using transfer learning, 2015.) consists of 35886 facial expression images, including test images , announcement verification g...

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Abstract

The invention relates to the technical field of target identification, and discloses an expression recognition method based on BN parameter transfer learning. The method includes constructing a facialexpression recognition BN model structure according to the relationship between the facial expressions and the action unit tags; secondly, calculating BN initial parameters by utilizing the BN parameters calculated by the human face source domain data set and the human face target domain data set respectively, obtaining final human face expression recognition BN parameters according to a migration mechanism, performing BN reasoning by utilizing a reasoning algorithm in a BN theory, and recognizing facial expressions. According to the invention, a transfer learning mechanism is fully utilizedto apply knowledge learned in a certain field to different but related fields; the method can effectively solve the problem of insufficient data volume of facial expression modeling samples caused byillumination, shooting angles and the like in facial expression recognition, reduces the influence of insufficient samples on parameter learning precision and recognition results, and can be widely applied to noisy and uncertain environments in which a large amount of face target data is difficult to obtain.

Description

technical field [0001] The invention relates to the application field of target recognition in artificial intelligence, image engineering, management science and engineering, and in particular to an expression recognition method based on BN parameter transfer learning. Background technique [0002] Bayesian network (BN) has practical application value in uncertainty modeling and decision support. Bayesian network parameter learning is to obtain all parameters through sample data and prior knowledge when the structure is known. The process of conditional probability distribution of network nodes. [0003] After the problem domain is transformed into a BN model representation, the BN theory can be used to complete the reasoning task. Among them, the Junction tree algorithm is one of the most widely used BN exact inference algorithms with fast calculation speed. Because BN organically combines the theoretical results of probability theory and graph theory, it is an effective ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/174G06V40/168G06V10/50G06F18/24
Inventor 郭文强黄梓轩候勇严徐成毛玲玲赵艳徐紫薇李梦然
Owner SHAANXI UNIV OF SCI & TECH
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