|
|
|
| Local binary pattern histogram projection based fast recognition on large-scale face database |
| FU Xiao-feng, WEI Wei |
| (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China) |
|
|
|
Abstract The conventional local binary pattern (LBP) based facial recognition method selects Chi square statistic as the dissimilarity measure for LBP histogram. In view of Chi square statistic's complexity and the high-dimensional recognition process, the conventional method is very slow as recognizing on large-scale face database. A method of LBP histogram projection (LBPHP) was proposed, which projects LBP histogram onto locality preserving projection (LPP) space and obtains low-dimensional LBPHP feature. Recognizing new sample only needs to compare its LBPHP feature with those of training samples. The process is simple and carried on low-dimensional space, thus the proposed method is fast and has good accuracy in the light of powerful discriminative property of LPP. Comparative experiments on two large-scale face databases demonstrated that the LBPHP method is superior to the conventional method on recognition speed. The LBPHP method is prominent especially on large-scale face database and suitable for practical application, e.g. identity authentication．
|
|
Published: 26 February 2010
|
|
|
大型人脸库上基于局部二元模式直方图映射的快速识别
传统的基于局部二元模式（LBP）的人脸识别方法采用卡方统计度量LBP直方图间的差异,由于卡方统计度量的复杂性以及是在高维空间进行判别,此方法在大型人脸库上的识别速度低,为此提出一种LBP直方图映射（LBPHP）方法.将LBP直方图映射到保局投影（LPP）空间获取低维LBPHP特征,当判别新样本时只须比较新样本与训练样本的LBPHP特征,识别过程简单且在低维空间进行,识别速度很快.鉴于LPP强大的鉴别特性,此方法的识别率很高.在2个知名人脸库上对LBPHP方法进行实验验证,结果表明,相比于传统识别方法,LBPHP的识别速度快,尤其在大型人脸库上优势更加明显,适于在此类人脸库上的实际应用如身份认证等.
|
|
| ［1］ 曹林,王东峰,刘小军,等. 基于二维Gabor小波的人脸识别算法［J］. 电子与信息学报, 2006, 28(3): 490-494．
CAO Lin, WANG Dong-feng, LIU Xiao-jun, et al. Face recognition based on two-dimensional Gabor wavelets ［J］. Journal of Electronics and Information Technology, 2006, 28(3): 490-494．
［2］ 张文超,山世光,张洪明,等. 基于局部Gabor变化直方图序列的人脸描述与识别［J］. 软件学报, 2006, 17(12): 2508-2517．
ZHANG Wen-chao, SHAN Shi-guang, ZHANG Hong-ming, et al. Histogram sequence of local Gabor binary pattern for face description and identification ［J］. Journal of Software, 2006, 17(12): 2508-2517．
［3］ 陈华杰,韦巍. 基于支持向量AAM迭代学习的性别分类算法［J］. 浙江大学学报：工学版, 2005, 39(12): 1989-1992．
CHEN Hua-jie, WEI Wei. Support vector AAM based iterative learning algorithm for gender classification ［J］. Journal of Zhejiang University: Engineering Science, 2005, 39(12): 1989-1992．
［4］ TURK M A, PENTLAND A P. Face recognition using eigenfaces ［C］∥ Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Maui: IEEE, 1991: 586-591．
［5］ 祝磊,朱善安. 基于二维广义主成分分析的人脸识别［J］.浙江大学学报：工学版, 2007, 41(2): 264-267．
ZHU Lei, ZHU Shan-an. Face recognition based on two-dimensional image principal component analysis ［J］. Journal of Zhejiang University: Engineering Science, 2007, 41(2): 264-267．
［6］BELHUMEUR P N, HESPANHA J P, KRIEGMAN D J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720．
［7］ HE X F, YAN S C, HU Y X, et al. Face recognition using Laplacianfaces ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340．
［8］AHONEN T, HADID A, PIETIKINEN M. Face description with local binary patterns: application to face recognition ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(12): 2037-2041．
［9］ 刘晓旻,谭华春,章毓晋. 人脸表情识别研究的新进展［J］. 中国图象图形学报, 2006, 11(10): 1359-1368．
LIU Xiao-min, TAN Hua-chun, ZHANG Yu-jin. New research advances in facial expression recognition［J］. Journal of Image and Graphics, 2006, 11(10): 1359-1368．
［10］YANG H, WANG Y. A LBP-based face recognition method with Hamming distance constraint ［C］∥ Fourth International Conference on Image and Graphics. Chengdu: IEEE, 2007: 645-649．
［11］ AHONEN T, PIETIKINEN M, HADID A, et al. Face recognition based on the appearance of local regions ［C］∥ Proceedings of the 17th International Conference on Pattern Recognition. Cambridge: IEEE, 2004, 3: 153-156．
［12］AHONEN T, HADID A, PIETIKINEN M. Face recognition with local binary patterns ［C］∥ Proceedings of the Eighth European Conference on Computer Vision. Prague: Springer, 2004: 469-481．
［13］OJALA T, PIETIKINEN M, MENP T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987．
［14］GAO W, CAO B, SHAN S, et al. The CAS-PEAL large-scale Chinese face database and evaluation protocols ［R］. Technical Report No. JDL_TR_04_FR_001. Joint Research and Development Laboratory, CAS, 2004.
［15］ PHILLIPS P J, WECHSLER H, HUANG J, et al. The FERET database and evaluation procedure for face recognition algorithms ［J］. Image and Vision Computing Journal, 1998, 16(5): 295-306．
［16］PHILLIPS P J, MOON H, RIZVI S A, et al. The FERET evaluation methodology for face recognition algorithms ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(10): 1090-1104． |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
