Expression recognition method based on reverse synergetic salient region features

An expression recognition and regional feature technology, applied in the field of expression recognition, can solve the problem of lack of correlation between expressions in a single expression image and the limitations of a single classifier

Active Publication Date: 2017-11-24
GUANGDONG UNIV OF TECH
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem of the lack of correlation between expressions and the limitation of a single classifier in the recognition of a single expression image, the present invention proposes an expression recognition method based on collaborative salient region features

Method used

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  • Expression recognition method based on reverse synergetic salient region features
  • Expression recognition method based on reverse synergetic salient region features
  • Expression recognition method based on reverse synergetic salient region features

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Embodiment Construction

[0018] 1. Data set preprocessing

[0019] It mainly combines the sneak algorithm and the GVF algorithm to detect the contour of the face, and retains the pixels in the contour of the face, excludes the pixels outside the contour, and resets the pixels outside the contour to 0. The result is as figure 1 As shown, the pure facial expression image is obtained after preprocessing.

[0020] 2. Extraction of salient areas of expressions

[0021] Collaborative saliency detection is divided into two parts: saliency detection and synergy detection. The saliency and synergy analysis are performed using cluster-level spatial features and contrast features respectively, and then the multiplicative feature fusion method is used to generate the expression collaboration saliency map.

[0022] The contrast feature reflects the uniqueness between a single image or multiple images, and is widely used in the saliency calculation of a single image. The present invention uses a cluster-based contrast fea...

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Abstract

The invention discloses an expression recognition method based on reverse synergetic salient region features. The whole recognition process is mainly divided into five parts, namely preprocessing, detection of an expression salient region, feature extraction on the salient region, weight assignment and recognition and classification. The method comprises the specific steps that (1) a face region is divided in a training test sample; (2) a reverse synergetic salient detection algorithm is utilized to extract the expression salient region from the divided part; (3) an LBP operator and an HOG operator are utilized to perform feature extraction on the salient region; (4) a support vector machine is utilized to perform preliminary classification on all salient local features, and weight assignment is performed; and (5) a multi-classification decision-making mechanism is used for recognition and classification. The method is combined with the relevancy between expressions, the relevancy is utilized to extract a local region containing rich expression information, and therefore the calculated amount is greatly reduced; and meanwhile, the multi-classification decision-making mechanism is used for classification, so that the recognition rate is increased accordingly.

Description

Technical field [0001] The invention belongs to the technical field of image processing, and particularly relates to an expression recognition method, which can be used for human-computer interaction and the like. Background technique [0002] Facial expression recognition is a branch of the field of pattern recognition. This research can fundamentally change the way of human-computer interaction, which is one of the current research hotspots. The main process of facial expression recognition technology is to first extract local regions with low dimensionality, high robustness and strong expression description ability, and then perform multi-feature fusion according to the degree of influence of different facial regions on different expressions, so as to improve the recognition rate And robustness enhancement. How to effectively extract local areas and fuse local information to improve the final decision accuracy has become a new research direction in the field of facial express...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/174G06V10/50G06V10/462G06V10/44G06F18/23G06F18/2411
Inventor 罗源张灵
Owner GUANGDONG UNIV OF TECH
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