Research and implementation of intelligent terminal lightweight face recognition system

Guo-jun MA; Hai-dong ZHOU

doi:10.11959/j.issn.1000-436x.2015294

您当前的位置：

首页 >

文章列表页 >

Research and implementation of intelligent terminal lightweight face recognition system

Academic paper | 更新时间：2024-06-05

- Research and implementation of intelligent terminal lightweight face recognition system
- Journal on Communications Vol. 36, Issue Z1, Pages: 149-156(2015)
- 作者机构：
  
  1. 西安文理学院信息工程学院，陕西西安 710065
  2. 西安电子科技大学通信工程学院，陕西西安 710071
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2015294
  CLC： TP391
- Online First：2015-11，
  
  Published：25 November 2015
- 稿件说明：
移动端阅览
Guo-jun MA, Hai-dong ZHOU. Research and implementation of intelligent terminal lightweight face recognition system[J]. Journal on Communications, 2015, 36(Z1): 149-156.
DOI：

Guo-jun MA, Hai-dong ZHOU. Research and implementation of intelligent terminal lightweight face recognition system[J]. Journal on Communications, 2015, 36(Z1): 149-156. DOI： 10.11959/j.issn.1000-436x.2015294.

摘要

针对传统人脸识别算法不能有效适用于智能移动终端的问题，提出一种基于经典SIFT算法的特征加权分簇匹配的轻量级改进方案，该方案能自动学习、自适应添加可靠的测试样本到训练样本空间，具有合理划分和科学权值分配特性，使该方案在识别率和运行时间上都有提高。改进算法分别在 ORL 人脸库和 Yale 人脸库做了测试，相对于经典SIFT算法识别率提升了6.13%和14.11%，运行效率提升了9.1%和4.7%。同时按照Zhou的测试方法，在ORL人脸库识别率达到74.05%，比PCA、LBP等经典算法都有明显的提升

并在Android智能终端中对识别方案做了实现，实验数据验证了改进算法在 Android 系统的可用性，最后提出一种基于云架构的改进方案。

Abstract

In order to solve this problem that traditional face recognition scheme was not efficiently suitable to intelligent terminal scene.An improved lightweight scheme of feature weighted clustering matching based on SIFT was presented.The scheme can learn automatically and adaptively add test samples to the training sample space.Reasonable division and scientific distribution of weight make this scheme has improved at run time and recognition rate.The improved algorithm has been tested in the ORL face database and Yale face database

compared with the classical SIFT algorithm the recognition rate improved by 6.13% and 14.11%

the running efficiency increased 9.1% and 4.7%.At the same time

in accordance with the test method by Zhou

in the ORL face database

the recognition rate was up to 74.05%

significantly improved than PCA

LBP and other classical algorithm.The algorithm in Android terminal is implemented

and the improved algorithm is verified to be available in the Android system by experiments.Finally

an improved scheme was proposed based on the cloud architecture.

关键词

Keywords

references

LI B , WU S , ZENG F T . Implementation of face identification in intelligent mobile telephone [J ] . Computer Engineering , 2006 , 32 ( 7 ): 272 - 274 .

LI W . Implementation of face identification in intelligent mobile telephone [J ] . Computer Technology and Development , 2008 , 18 ( 1 ): 161 - 163 .

HU X P , CHEN D S , CHEN W B . Design and implementation of face detection system on smart phone [J ] . Computer Engineering and Design , 2010 , 31 ( 3 ): 672 - 675 .

CHEN Y N , CHEN X G . A novel identify recognition system based on mobile phone and face recognition [J ] . Computer Application Software , 2011 , 28 ( 3 ): 77 - 79 .

AHONEN T , HADID A,PIETIKÄINEN M . Face Recognition With Local Binary Patterns [M ] . Computer vision-eccv 2004 . Springer Berlin Heidelberg , 2004 . 469 - 481 .

LOWE D G . Distinctive image features from scale-invariant keypoints [J ] . International Journal of Computer Vision , 2004 , 60 ( 2 ): 91 - 110 .

LOWE D G . Object recognition from local scale-invariant features [A ] . The Proceedings of the Seventh IEEE International Conference on Computer vision [C ] . 1999 . 1150 - 1157 .

CAO S . A fast SURF way for human face recognition with cell similarity [A ] . 2011 6th IEEE Conference on Industrial Electronics and Applications (ICIEA) [C ] . 2011 . 166 - 169 .

ZHOU Z M , YU S Y , ZHANG Z R . An algorithm for face recognition based on sift descriptor [J ] . Journal of Image and Graphics , 2008 , 13 ( 10 ): 1882 - 1885 .

LUO J , SHI Y X , DUAN D Y . Face recognition method based on SIFT feature [J ] . Computer Engineering , 2010 , 36 ( 13 ): 173 - 174 .

HUANG A . Similarity measures for text document clustering [A ] . Proceedings of the Sixth New Zealand Computer Science Research Student Conference (NZCSRSC2008) [C ] . Christchurch,New Zealand , 2008 . 49 - 56 .

STREHL A , GHOSH J , MOONEY R . Impact of similarity measures on web-page clustering [A ] . Workshop on Artificial Intelligence for Web Search (AAAI 2000) [C ] . 2000 . 58 - 64 .

ALY M . Face Recognition Using SIFT Features [R ] . CNS/Bi/EE Report , 2006 . 186 - 193 .

ZHAO R Z . Study on Face Recognition Based on Single-sample Training Set [D ] . Chongqing:Chongqing University , 2012 .

Views

2233

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Lightweight authentication key agreement protocol for Internet of vehicles

Lightweight image super-resolution network based on muti-domain information enhancement

Lightweight and secure search scheme for medical data sharing

Lightweight PUF-based anonymous authentication protocol in V2G

Lightweight Transformer traffic scene semantic segmentation algorithm integrating multi-scale depth convolution

Related Author

ZHOU Yi

WANG Pengchao

CHEN Dongdong

PANG Xiaohui

LIU Yali

JIANG He

KOU Qiqi

LIU Gui

Related Institution

School of Cyber Science and Engineering, Southeast University

Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology

State Key Laboratory for Novel Software Technology, Nanjing University

College of Computer Science and Technology, Jiangsu Normal University

School of Information and Control Engineering, China University of Mining and Technology

AI问答

⁰