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Expression recognition method based on classifier selective integration

An expression recognition and classifier technology, which is used in character and pattern recognition, acquisition/recognition of facial features, instruments, etc., can solve the problems of increasing the amount of calculation and the performance of the integrated classifier is not greatly improved, and achieves improved generalization ability. Effect

Active Publication Date: 2020-12-22
GUIZHOU UNIV
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Problems solved by technology

In addition, even classifiers generated by different algorithms sometimes have similarities. Similar classifiers do not substantially improve the performance of integrated classifiers, but instead increase the amount of computation.

Method used

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  • Expression recognition method based on classifier selective integration
  • Expression recognition method based on classifier selective integration
  • Expression recognition method based on classifier selective integration

Examples

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Embodiment 1

[0042]Using the above scheme, taking the JAFFE expression data as an example, the expression picture data is divided into training set and test set in proportion, and some expression pictures in the database are randomly selected for processing (such as adding noise, rotating, etc.) as a verification set. In the process of generating the classifier pool, a variety of machine learning algorithms are selected as classifiers to train the training set images, and a large number of individual learners are generated by adjusting the classifier parameters based on the verification set. The performance and diversity of classifiers are evaluated by the accuracy rate and kappa coefficient value; in the process of classifier sequence selection, the individual learners are first sorted in descending order according to the accuracy rate of the verification set, and the initial classifier sequence S is Ф, if there is an accuracy rate greater than or equal to T 1 (if take T 1 = 1), select i...

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Abstract

The invention provides an expression recognition method based on classifier selective integration. The expression recognition method comprises the following steps: (1) generating a classifier pool; (2) evaluating the ability of an individual learner; (3) selecting a classifier sequence; (4) carrying out decision layer fusion. According to the invention, capability evaluation is carried out on prediction accuracy of individual learners and differences among different individual learners, and the capability evaluation is used as an evaluation standard of a selective integration algorithm for thecapability of the individual learners; an individual learner set with a better classification effect and better diversity can be selected according to the specific judgment condition of the verification sample, and the generalization ability of a classification system can be effectively improved when the expression data of batch unknown category labels is predicted.

Description

technical field [0001] The invention relates to an expression recognition method based on selective integration of classifiers. Background technique [0002] The facial expression of the human face reflects the rich emotional information of the human face and is the main way of human non-verbal communication. Facial expression recognition is a challenging research topic in the field of computer vision, and it is of great significance in psychology and human-computer interaction research. Facial expression recognition is to extract and analyze the facial features of the human face, classify and understand them according to people's thinking and cognitive methods, use computers to analyze and learn a large number of facial data features and prior knowledge, and then learn from facial information To analyze and understand people's emotions, such as happiness, surprise, fear, anger, disgust, sadness and so on. Using computers to recognize facial expressions is the basis for re...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/174G06V40/172G06N3/045G06F18/217
Inventor 李丹杨唐玉梅陈靖宇邹晓瑜周西川史鹏程
Owner GUIZHOU UNIV
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