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计算机系统应用英文版:2020,29(2):157-162

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基于多Inception结构的卷积神经网络人脸识别算法

(1.福建师范大学光电与信息工程学院, 福州 350007;2.福建师范大学医学光电科学与技术教育部重点实验室, 福州 350007;3.福建师范大学福建省光子技术重点实验室, 福州 350007;4.福建师范大学福建省光电传感应用工程技术研究中心, 福州 350007)

Face Recognition Algorithms Based on Convolutional Neural Network with Multi-Inception Structure

LI Nan¹, CAI Jian-Yong^1,2,3,4, LI Ke¹, CHENG Yu¹, ZHANG Ming-Wei¹

(1.College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China;2.Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China;3.Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China;4.Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China)

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本文已被：浏览 2021次下载 2156次
Received:June 18, 2019 Revised:July 12, 2019

中文摘要: 人脸识别是视觉识别的一个重要领域，由于人脸识别尺度变化范围大，光照、姿态变化剧烈以及遮挡问题，导致该类非限制条件下的识别难度较大，为了解决该类问题，本文提出了一种基于Tensorflow平台的多Inception模型，通过将多个Inception结构进行串联，再通过分解卷积核的方式减少输入参数，实现了多维度同时卷积再聚合，提高了人脸识别的精度.实验结果表明，该方法在较少参数的条件下能提取出更具区分度的人脸特征，与分类损失方法及融合了其他度量学习方式的方法相比，提高了识别准确率，减少了计算时间.

中文关键词: 人脸识别 Tensorflow Inception 卷积神经网络

Abstract:Face recognition is an important field of visual recognition. Because of the large scale of variations in face recognition, namely drastic changes in illumination and pose, occlusion problems, and complex image background, it is difficult to recognize the face under such unrestricted conditions. In order to solve these problems, a multi-Inception model based on Tensorflow platform is proposed in this study. By combining multiple Inception knots, a multi-Inception-V3 model based on Tensorflow platform is proposed. The structure is connected in series, which realizes the convolution and re-aggregation of multiple dimensions at the same time, and improves the accuracy of face recognition. The experimental results show that the proposed method can extract more discriminant face features with fewer parameters. Compared with the classification loss method and the fusion of other metric learning methods, it improves the accuracy of face recognition under unconstrained conditions.

keywords: face recognition Tensorflow Inception convolution neural network

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Author Name	Affiliation	E-mail
LI Nan	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
CAI Jian-Yong	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China	cjy@fjnu.edu.cn
LI Ke	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
CHENG Yu	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
ZHANG Ming-Wei	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China

Author Name	Affiliation	E-mail
LI Nan	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
CAI Jian-Yong	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China	cjy@fjnu.edu.cn
LI Ke	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
CHENG Yu	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
ZHANG Ming-Wei	College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China

引用文本：
李楠,蔡坚勇,李科,程玉,张明伟.基于多Inception结构的卷积神经网络人脸识别算法.计算机系统应用,2020,29(2):157-162
LI Nan,CAI Jian-Yong,LI Ke,CHENG Yu,ZHANG Ming-Wei.Face Recognition Algorithms Based on Convolutional Neural Network with Multi-Inception Structure.COMPUTER SYSTEMS APPLICATIONS,2020,29(2):157-162