Low-resolution image facial expression recognition method based on feature reconstruction model

A low-resolution image, facial expression recognition technology, applied in the field of facial image expression recognition, can solve the problems of large calculation amount of reconstructed face image and easy privacy leakage.

Active Publication Date: 2021-05-18
XI AN JIAOTONG UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to overcome the shortcomings of large amount of calculation and easy disclosure of priv...

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  • Low-resolution image facial expression recognition method based on feature reconstruction model
  • Low-resolution image facial expression recognition method based on feature reconstruction model
  • Low-resolution image facial expression recognition method based on feature reconstruction model

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Embodiment Construction

[0065] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0066] It should be noted that the terms "first" and "second" in the description and claims of the present invention and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circ...

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Abstract

The invention discloses a low-resolution image facial expression recognition method based on a feature reconstruction model, and belongs to the field of facial image expression recognition. The method comprises the following steps: constructing training and testing data sets; training a facial expression recognition model of the feature reconstruction model, extracting image expression features by using a feature extraction network with fixed parameters, training the model by using a generative adversarial network mode to obtain an expression feature generator and a feature discriminator, and reconstructing features by using FSRG as an input image to obtain FSR; classifying the feature FSR by a classifier consisting of a full-connection network and a softmax function layer, and re-weighting sample loss by using a probability value of a correct category corresponding to a sample output by the softmax layer; the invention is not sensitive to the resolution of the input image, the recognition accuracy under the low resolution is improved, and the recognition effect on each resolution is more stable.

Description

technical field [0001] The invention belongs to the field of face image expression recognition, in particular to a low-resolution image face expression recognition method based on a feature reconstruction model. Background technique [0002] Facial expressions are one of the most direct and natural signals for humans to express emotions. Facial expression recognition is a hot topic in the research of natural human-computer interaction, computer vision, emotional computing and image processing, and has a wide range of applications in the fields of human-computer interaction, distance education, security, intelligent robot development, medical treatment, animation production, etc. . [0003] In different scenarios, due to changes in equipment and environment and the imaging principle of pinhole cameras, the facial images of people in multi-person photography scenarios have different resolutions of "near large, far small". Compression reduces the quality and resolution of an ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08G06T3/40
CPCG06N3/084G06T3/4053G06V40/174G06V10/462G06N3/047G06F18/2415G06F18/214
Inventor 田锋经纬南方洪振鑫郑庆华
Owner XI AN JIAOTONG UNIV
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