[1]陈雯柏,黄至铖,刘琼.一种基于P稳定局部敏感哈希算法的相似人脸检索系统设计[J].智能系统学报,2017,12(3):392-396.[doi:10.11992/tis.201607005]
CHEN Wenbai,HUANG Zhicheng,LIU Qiong.A similar-face-image-retrieval system design based on a P-stable locality-sensitive Hashing algorithm[J].CAAI Transactions on Intelligent Systems,2017,12(3):392-396.[doi:10.11992/tis.201607005]
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CHEN Wenbai,HUANG Zhicheng,LIU Qiong.A similar-face-image-retrieval system design based on a P-stable locality-sensitive Hashing algorithm[J].CAAI Transactions on Intelligent Systems,2017,12(3):392-396.[doi:10.11992/tis.201607005]
一种基于P稳定局部敏感哈希算法的相似人脸检索系统设计
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第3期
页码:
392-396
栏目:
学术论文—机器感知与模式识别
出版日期:
2017-06-25
- Title:
- A similar-face-image-retrieval system design based on a P-stable locality-sensitive Hashing algorithm
- Keywords:
- face-image retrieval; locality-sensitive Hashing algorithm; P-stable distribution; locally assembled binary feature
- 分类号:
- TP18;TN911.22
- 摘要:
- 针对智能移动终端、移动机器人安防巡检等应用需求，本文提出了一种基于P稳定局部哈希算法的相似人脸检索系统设计。首先，采用基于局部组合二值特征检测图像中的人脸。进而，通过深度自编码神经网络提取人脸特征。最后，基于所提取的图像的人脸区域特征使用稳定分布的局部敏感哈希算法对每幅图像构建高效索引。实验表明，本文所设计的相似人脸检索系统处理一幅图像的时间约400 ms，能满足实际应用需求，且返回检测结果的误检率低于经典AdaBoost算法。
- Abstract:
- This paper proposes a similar-face-retrieval system based on a P-stable local hashing algorithm to meet the requirements of intelligent mobile terminals and mobile-robot-security inspection applications. First, our system extracts a locally assembled binary feature to detect a human face in a particular image. Subsequently, a deep auto-encoding network is used to compute the subject’s facial features. Finally, a locality-sensitive hashing algorithm based on a P-stable distribution is employed to construct an efficient index for each image according to the facial features. Our test results show that the proposed similar-face-image-retrieval system can process images within approximately 400 ms, thereby meeting the requirements of practical biometric applications. In addition, the false detection rate of the proposed method is considerably low than that of the classical AdaBoost algorithm.
- 参考文献/References:
-
[1] CHEN B C, CHEN Y Y, KUO Y H, et al. Scalable face image retrieval using attribute-enhanced sparse code words [J]. IEEE transactions on multimedia, 2013,15 (5) :1163-1173.
[2] 胡正平,李静.基于低秩子空间恢复的联合稀疏表示人脸识别算法[J].电子学报, 2013, 41(5): 987-991.HU Zhengping, LI Jing. Face recognition of joint sparse representation based on lowmark subspace recovery [J]. Chinese journal of electronics, 2013, 41(5): 987-991.
[3] XIE S F, SHAN S G, CHEN X L, et al. Fusing local patterns of gabor magnitude and phase for face recognition[J]. IEEE trans on image processing, 2010,19(5):1349-1361.
[4] LI Y, WANG R, LIU H, et al. Two birds, one stone: jointly learning binary code for large-scale face image retrieval and attributes prediction[C]//IEEE International Conference on Computer Vision. IEEE Computer Society, 2015:3819-3827.
[5] BURGOS-ARTIZZU X P, PERONA P, DOLLAR P. Robust face landmark estimation under occlusion[C]//International Conference on Computer Vision, 2013:1513-1520.
[6] SHAN S G, CHANG Y Z, GAO W, et al. Curse of misalignment in face recognition: problem and a novel mis alignment learning solution[C]//Proceeding of the 6th International Conference on Face and Gesture Recognition, Jeju, Korea, 2004, 314-320.
[7] GUTTMAN A. A dynamic index structure for spatial searching[C]//Sigmod’84, Proceedings of Meeting, Boston, Massachusetts, 1984: 47-57.
[8] CHEN D, CAO X, WEN F, et al.Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification[C]//Proceeding of the Computer Vision and Pattern Recognition. Piscataway, 2013: 3025-3032.
[9] GIONIS A, INDYK P, MOTWANI R. Similarity Search in High Dimensions via Hashing[C]//International Conference on Very Large Data Bases. Morgan Kaufmann Publishers Inc, 2000: 518-529.
[10] SUN Y, WANG X G, TANG X O. Deep convolutional network cascade for facial point detection[C]//Proceeding of the Computer Vision and Pattern Recognition, 2013: 121-125.
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[12] YAN S, SHAN S, Chen X, et al. Locally Assembled Binary (LAB) feature with feature-centric cascade for fast and accurate face detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2008: 1-7.
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备注/Memo
收稿日期:2016-07-05。
基金项目:北京高等学校高水平人才交叉培养“实培计划”项目（京教高〔2015〕11号）.
作者简介:陈雯柏，男，1975年生，副教授，博士，中国人工智能学会理事, 主要研究方向为机器人控制与无线传感器网络;黄至铖，男，1992年生，硕士研究生，主要研究方向为机器学习与模式识别;刘琼，女，1984年生，副教授，主要研究方向为模式识别、认知计算、机器学习.
通讯作者:陈雯柏.E-mail:chenwb03@126.com.
更新日期/Last Update:
2017-06-25