A BPD Facial Emotion Recognition Method Based on Improved Residual Network

An emotion recognition and network technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve problems such as poor recognition accuracy and poor generalization ability, and achieve accurate emotion recognition, high recognition The effect of training efficiency

Active Publication Date: 2022-07-08
山西三友和智慧信息技术股份有限公司
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Problems solved by technology

[0004] In view of the above-mentioned technical problems of the existing facial emotion recognition technology that requires manual selection of features, poor recognition accuracy and poor generalization ability, the present invention provides an improved residual error based algorithm with high automation, high recognition accuracy and strong generalization ability. BPD Facial Emotion Recognition Method Based on Network

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  • A BPD Facial Emotion Recognition Method Based on Improved Residual Network
  • A BPD Facial Emotion Recognition Method Based on Improved Residual Network

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Embodiment Construction

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] A BPD facial emotion recognition method based on improved residual network, comprising the following steps:

[0031] S1. Collect data and build a data set;

[0032] S2, label reconstruction;

[0033] S3. Data set segmentation;

[0034] S4, model construction;

[0035] S5, model training;

[0036] S6, model verification;

[0037] S7. Model evaluation.

[0038] Further, the data in the data set in S1 includes a data label;...

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Abstract

The invention belongs to the technical field of facial recognition, in particular to a BPD facial emotion recognition method based on an improved residual network, comprising the following steps: collecting data, constructing a data set; label reconstruction; data set segmentation; model construction; model training; model Validation; Model Evaluation. The invention improves ResNet101 by using the Swish activation function, and modifies the network into a multi-task network, improves the model training efficiency through the internal connection between different tasks, helps the model have higher recognition accuracy, and conducts facial emotion analysis on BPD people. The multi-angle recognition can capture more in-depth features of facial emotion data of BPD crowd, and perform more accurate emotion recognition. The present invention is used for facial emotion recognition.

Description

technical field [0001] The invention belongs to the technical field of facial recognition, in particular to a BPD facial emotion recognition method based on an improved residual network. Background technique [0002] Most of the existing emotion recognition technologies need to be combined with scene features for recognition, and most of the features need to be manually selected. The facial emotion recognition for specific groups such as BPD (borderline personality disorder) people is different from the emotional characteristics of normal people. Technology cannot identify the emotions of such people with high accuracy. [0003] Problems or defects in the existing technology: the existing facial emotion recognition technology has the problems of requiring manual selection of features, poor recognition accuracy, and poor generalization ability when identifying specific groups of people. SUMMARY OF THE INVENTION [0004] Aiming at the above-mentioned technical problems of m...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V40/16G06V10/82G06N3/04G06N3/08
CPCG06N3/08G06V40/174G06V40/172G06V40/168G06N3/048
Inventor 潘晓光令狐彬董虎弟李娟陈智娇
Owner 山西三友和智慧信息技术股份有限公司
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