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Face expression analysis method and system based on a sequencing convolutional neural network

A technology of convolutional neural network and facial expression, which is applied in the field of facial expression analysis method and system based on sorting convolutional neural network, can solve the problems of low accuracy of expression intensity estimation and susceptibility to noise interference, and eliminate individual differences , Eliminate environmental noise, improve accuracy and robustness

Active Publication Date: 2019-04-19
HUAZHONG NORMAL UNIV
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Problems solved by technology

At present, many studies have been carried out in this field, but there are still problems such as low accuracy of expression intensity estimation and susceptibility to noise interference.

Method used

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  • Face expression analysis method and system based on a sequencing convolutional neural network
  • Face expression analysis method and system based on a sequencing convolutional neural network
  • Face expression analysis method and system based on a sequencing convolutional neural network

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[0078] Using the CK+ expression library created by Carnegie Mellon University, it contains 123 adults aged 18-30, with a total of 593 expression sequences, of which 65% are female and 35% are male. The present invention selects 96 people from 123 people, and each person has at least 2 basic expression sequences that can be used for splicing, 64 people are selected for training, and the remaining 32 people are used for testing. The specific implementation steps are as follows:

[0079] 1. Preprocessing the facial expression images

[0080] (1.1) Use the Haar-like feature and adaboost learning algorithm proposed by Viola and Jones to detect the face area of ​​each expression image;

[0081] (1.2) Perform affine transformation on the face image extracted in step (1.1) to realize image scale normalization and face alignment. After transformation, the sizes of all images are normalized to 224×224, and the center coordinates of the eyes in all images remain the same. The coordinat...

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Abstract

The invention discloses a face expression analysis method and system based on a sorting convolutional neural network, and belongs to the field of image processing and pattern recognition. According tothe invention, the interested expressions are spliced with other types of expressions; Any two frames of images are selected from the spliced facial expression sequence to serve as input, the expression intensity sorting model is trained through the sorting convolutional neural network, intensity estimation of interested expressions can be achieved through the single expression intensity sortingmodel, and estimation of expression categories can be achieved through combination of multiple expression intensity sorting models. According to the method, the category and intensity of expressions can be estimated at the same time, individual difference and environmental noise are eliminated while facial expression information is reserved to the maximum extent, and therefore the correctness androbustness of expression analysis are improved, and the method has extremely high practical application prospects.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and in particular relates to a human facial expression analysis method and system based on a sorting convolutional neural network. Background technique [0002] Facial expression analysis is a comprehensive subject involving pattern recognition, image processing, artificial intelligence and other disciplines. The so-called facial expression analysis refers to the process of allowing the computer to extract features from a given expression image, and combine the prior knowledge of humans to carry out learning, reasoning, and judgment, and then understand the process of human emotions. Facial expression analysis is widely used in emotional computing, human-computer interaction, emotional robotics, medical care and other fields, and is a current research hotspot. [0003] Facial expression analysis is mainly composed of two parts: expression recognition and expressi...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/174G06V40/172G06V40/161G06N3/045
Inventor 张坤陈靓影韩加旭徐如意刘乐元彭世新刘小迪
Owner HUAZHONG NORMAL UNIV
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