Research on Inception Module Incorporated Siamese Convolutional Neural Networks to Realize Face Recognition

doi:10.3969/j.issn.0372-2112.2020.04.004

您当前的位置：

首页 >

文章列表页 >

Research on Inception Module Incorporated Siamese Convolutional Neural Networks to Realize Face Recognition

更新时间：2025-07-08

- Research on Inception Module Incorporated Siamese Convolutional Neural Networks to Realize Face Recognition
- Acta Electronica Sinica Vol. 48, Issue 4, Pages: 643-647(2020)
- 作者机构：
  
  长安大学电子与控制工程学院,陕西,西安,710064
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.04.004
  CLC： TP391.41
- Published Online：25 April 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
Research on Inception Module Incorporated Siamese Convolutional Neural Networks to Realize Face Recognition[J]. Acta Electronica Sinica, 2020, 48(4): 643-647.
DOI：

Research on Inception Module Incorporated Siamese Convolutional Neural Networks to Realize Face Recognition[J]. Acta Electronica Sinica, 2020, 48(4): 643-647. DOI： 10.3969/j.issn.0372-2112.2020.04.004.

摘要

针对非限定性条件下人脸识别困难问题，设计了一种引入感知模型的改进孪生卷积神经网络结构（Inception Module Incorporated Siamese Convolutional Neural Networks，IMISCNN），在充分利用孪生结构有效减少外界干扰并避免过拟合等优点的基础上，为其增加感知模型实现更丰富特征的提取.为寻找最优学习率引入了循环学习率策略，加速模型收敛.在CASIA-webface和Extended Yale B标准人脸数据库上的仿真实验表明，所提IMISCNN算法提升了人脸识别精度.

Abstract

To solve the problem of face recognition under unlimited conditions

a simple network structure named Inception Module Incorporated Siamese Convolutional Neural Networks (IMISCNN) was designed

which was suitable for small-scale data sets. On the basis of making full use of the Siamese structure to effectively reduce external interference and avoid over-fitting

inception module was incorporated to the Siamese network to extract richer features. Furthermore

a cyclical learning rate strategy was adopted to accelerate the convergence of the model. Simulation results on the CASIA-webface and Extended Yale B standard face database showed that the recognition accuracy of IMISCNN was significantly improved compared with other face recognition algorithms.

关键词

Keywords

references

Views

158

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Lightweight New Fesidual Face Recognition Method Based on Feature Fusion

Multi-task Learning and Identity-constrained Generative Adversarial Network for Face Frontalization and Recognition

A Kernel Orthogonal Discriminant Locality Preserving Projections Method

Matrix Pattern Based Minimum Within-Class Scatter Support Vector Machines

A New Approach for Face Recognition Based on Singular Value Features and Neural Networks

Related Author

HUI Kang-hua

YAN Jian-qing

GAO Si-hua

HE Huai-qing

HUANG Xin-yan

LIU Fang

BAO Qian-yue

LI Ren-peng

Related Institution

College of Computer Science and Technology， Civil Aviation University of China

Joint International Research Laboratory of Intelligent Perception and Computation， Xidian University

International Research Center for Intelligent Perception and Computation， Xidian University

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education， Xidian University

School of Artificial Intelligent， Xidian University

⁰