轻量级智能终端人脸识别系统研究与实现

马国峻; 周海东

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

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轻量级智能终端人脸识别系统研究与实现

学术论文 | 更新时间：2024-06-05

- 轻量级智能终端人脸识别系统研究与实现
- Research and implementation of intelligent terminal lightweight face recognition system
- 通信学报 2015年36卷第Z1期页码：149-156
- 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：
  
  [ "马国峻（1978-），男，安徽无为人，西安文理学院讲师，主要研究方向为信息安全、数字内容保护、嵌入式系统。" ]
  [ "周海东（1989-），男，甘肃两当人，西安电子科技大学硕士生，主要研究方向为信息安全、传感器网络。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2015294
  中图分类号： TP391
- 网络首发：2015-11，
  
  纸质出版：2015-11-25
- 稿件说明：
移动端阅览
马国峻, 周海东. 轻量级智能终端人脸识别系统研究与实现[J]. 通信学报, 2015,36(Z1):149-156.

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.
马国峻, 周海东. 轻量级智能终端人脸识别系统研究与实现[J]. 通信学报, 2015,36(Z1):149-156. DOI： 10.11959/j.issn.1000-436x.2015294.

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

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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 .

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