Micro expression recognition method based on 3D convolution neural network

A convolutional neural network and recognition method technology, applied in the field of image processing and pattern recognition, can solve complex problems, and achieve the effect of enhancing robustness, reducing complexity, and reducing difficulty

Active Publication Date: 2017-04-19
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a method to solve the problems of complex feature extraction and feature dimensionality reduction in traditional micro-expression recognition, extract features from the dimensions of space an

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  • Micro expression recognition method based on 3D convolution neural network
  • Micro expression recognition method based on 3D convolution neural network
  • Micro expression recognition method based on 3D convolution neural network

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Embodiment Construction

[0039] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0040] Such as figure 1 and figure 2 As shown, the present invention designs a micro-expression recognition method based on a 3D convolutional neural network. In the actual application process, the micro-expression recognition method is realized based on a 3D convolutional neural network model (3D-CNN). The network model (3D-CNN) includes hardwired layer H1 (hardwired layer), convolutional layer C1, downsampling layer S1, convolutional layer C2, downsampling layer S2, convolutional layer C3, fully connected Layer, classification layer (Softmax classification layer); For the steps 001 to 009 designed below, first adopt random diagonal Levenberg-Marquardt optimization method to train the model parameters of the 3D convolutional neural network model (3D-CNN), Then, after performing the step 001, the trained 3D convolutio...

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Abstract

The invention relates to a micro expression recognition method based on a 3D convolution neural network. Based on a constructed 3D convolution neural network (3D-CNN) model, happiness, disgust, depression, surprise as well as five other micro expressions can be recognized effectively. The designed micro expression recognition method is simple and efficient. There is no need to carry out a series of processes such as feature extraction, feature dimension reduction and classification on sample data. The difficulty of preprocessing is reduced greatly. Through receptive field and weight sharing, the number of parameters needing to be trained by the neural network is reduced, and the complexity of the algorithm is reduced greatly. In addition, in the designed micro expression recognition method, through down-sampling operation of a down-sampling layer, the robustness of the network is enhanced, and image distortion to a certain degree can be tolerated.

Description

technical field [0001] The invention relates to a micro-expression recognition method based on a 3D convolutional neural network, which belongs to the technical field of image processing and pattern recognition. Background technique [0002] Micro-expressions are special facial expressions that reflect a person's true inner emotions. It is difficult for people to detect micro-expression with the naked eye, and its duration is very short and its intensity is very weak, about 1 / 25s-1 / 5s. Some researchers also believe that its duration is less than 450ms. Due to these characteristics of micro-expression, it has a wide application prospect in the fields of polygraph detection, clinical diagnosis and interrogation. [0003] In the early days, researchers were studying micro-expressions through psychological methods, and they all focused on the recognition of individual micro-expressions. The first micro-expression training tool, METT (Micro Expression Training Tool), was creat...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/174G06N3/045G06F18/2414
Inventor 卢官明杨成闫静杰
Owner NANJING UNIV OF POSTS & TELECOMM
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