An affective classification method based on deep forest and transfer learning

Abstract

The invention provides an emotion classification method based on deep forest and transfer learning. Firstly, a source domain data set and a training target domain data set are selected. Then the deepconvolution neural network is used to train the data set in the source domain, and the trained feature extraction model is obtained and saved. Preprocessing the training target domain data, wherein the preprocessing comprises channel conversion and size clipping; The feature extraction model is used to extract features from the preprocessed training target domain data set to obtain sample features. The sample characteristics are used as the input of the deep forest classification model, and the deep forest classification model is trained. When the training of the depth forest model classification model is completed, the trained depth forest classification model is used to classify the facial emotion images that actually need to be processed, and the classification result of the facial emotion images is obtained. The invention has the advantages that the classification efficiency is improved, the classification cost and the demand for training samples are reduced.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an emotion classification method based on deep forest and transfer learning. Background technique [0002] With the development of science and technology and the progress of society, the level of computer technology and artificial intelligence technology is getting higher and higher, and the degree of automation in society is also increasing, and people's demand for human-computer interaction is becoming stronger and stronger. In people's face-to-face communication, facial expressions and other body movements can convey non-verbal information, which can be used as a language aid to help the listener infer the speaker's intention. Facial expression is a means to express human cognition, emotion and state. It contains a lot of individual behavior information and is a complex expression set of individual characteristics, and these characteristics are often related to people's mental ...

AI Technical Summary

Problems solved by technology

In addition, traditional machine learning methods are also difficult to construct a unified and universal model according to people's diverse needs

To put it simply: (1) Since traditional machine learning algorithms rely heavily on a large number of training samples, if there are insufficient training samples, it will seriously affect the classification accuracy and speed

(2) There are many parameters in the above algorithm, and the parameter adjustment process is cumbersome, and it is difficult to adjust each parameter to an appropriate value

(3) The above classification methods can only establish specific models for specific tasks, and it is difficult to generalize between similar tasks

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