Facial expression recognition method based on regional self-attention convolutional neural network

A technology of convolutional neural network and facial expression recognition, which is applied in the field of facial expression recognition, can solve problems such as resource inclination in areas with significant facial expressions, inability to distinguish areas with prominent facial expressions, etc., to achieve enhanced robustness and enhanced influence Weights, performance-enhancing effects

Pending Publication Date: 2022-08-02
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, the deep network usually treats the input image as a whole, and cannot distinguish

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  • Facial expression recognition method based on regional self-attention convolutional neural network
  • Facial expression recognition method based on regional self-attention convolutional neural network
  • Facial expression recognition method based on regional self-attention convolutional neural network

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Embodiment Construction

[0053] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0054] The technical scheme that the present invention solves the above-mentioned technical problems is:

[0055] as attached figure 1 As shown, a facial expression recognition method based on regional self-attention convolutional neural network includes the following steps:

[0056] 1. As attached figure 1 As shown, input the original expression image into the feature extraction network based on VGG16, and extract the deep global features of the input expression image, including:

[0057] A1: The facial expression image is detected by the face detection and alignment network MTCNN to detect the key points of the face, and the face image is aligned and cropped into an input image I with a s...

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Abstract

The invention relates to a facial expression recognition method based on a regional self-attention convolutional neural network, which belongs to the technical field of pattern recognition and computer vision and comprises the following steps of: firstly, extracting deep global features of an input image by using a VGG16 network, and reserving global information of facial expressions while extracting the features; secondly, dynamically clustering the pixels through a designed regional local multi-valued mode and an improved K-means algorithm, ensuring the robustness of expression change regional features, expanding a binary mode to a plurality of modes, integrating gray difference information among the pixels in the region, and enhancing texture description; in addition, region weights are formed through a self-attention mechanism, and the weights of different regions are constrained by using rank regularization loss. And finally, combining the weighted features with the features extracted by the deep network to enhance the characterization capability of the features. The invention aims to establish a robust facial expression recognition network to accurately estimate the category of facial expressions in a real environment.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and computer vision, in particular to a facial expression recognition method. Background technique [0002] In recent years, artificial intelligence (AI) has developed vigorously, and the application of artificial intelligence in the grassroots of society is giving birth to a profound socio-technical change. The government and artificial intelligence scholars have recognized the key points of artificial intelligence, which has become the core driving force of a new round of industrial transformation and industrial revolution, and is crucial to the transformation and upgrading of the world economic structure. With the continuous in-depth exploration of artificial intelligence technology, it has gradually become well known to the public. In particular, major technology companies commercialize and apply related technologies to various industries such as transportation, healthcare, financ...

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Application Information

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IPC IPC(8): G06V40/16G06V10/82G06V10/80G06V10/762G06V10/54G06N3/04G06K9/62
CPCG06V40/174G06V40/172G06V40/176G06V10/806G06V10/82G06V10/54G06V10/763G06N3/045G06F18/23213
Inventor 周丽芳王懿江志程丁相栗思秦邓广
Owner CHONGQING UNIV OF POSTS & TELECOMM
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