Computer Science ›› 2018, Vol. 45 ›› Issue (6): 308-313.doi: 10.11896/j.issn.1002-137X.2018.06.054

Special Issue: Face Recognition

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Fast Face Recognition Algorithm Based on Local Fusion Feature and Hierarchical Incremental Tree

ZHONG Rui1,2, WU Huai-yu1, HE Yun1   

  1. School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081,China1;
    School of Mathematics and Computer Science,Gannan Normal University,Ganzhou,Jiangxi 341000,China2
  • Received:2017-01-05 Online:2018-06-15 Published:2018-07-24

Abstract: The off-line training and the high dimension of facial features in face recognition lead to the difficulty of achieving real-time processing performance.To solve this problem,the local fusion features and the hierarchical incremental tree were applied to construct a fast face recognition algorithm.Firstly,the supervised descent method(SDM) is used to locate the facial feature points.The feature of multi block-center symmetric local binary patterns(MB-CSLBP) in the neighborhood of each facial feature point is extracted and fused in series,which constitutes the proposed facial feature of local fusion feature of MB-CSLBP(LFP-MB-CSLBP).Then the above facial feature is sent into hierarchical incremental tree(HI-tree).Because the hierarchical clustering algorithm is used in the HI-tree to achieve incremental learning,it can train the recognition model online.Finally,the recognition rate and consuming time of the proposed algorithm are tested on three face databases and real application of video-based face recognition.The experimental results show that the proposed algorithm has better real-time computation and accuracy compared with other current approaches.

Key words: Hierarchical incremental tree, Incremental learning, Local fusion feature of MB-CSLBP, MB-CSLBP feature

CLC Number: 

  • TP391.41
[1]BECKER B C,ORTIZ E G.Evaluating open-universe face identification on the Web[C]//IEEE Conference on Computer Vision and Pattern Recognition.2013:904-911.
[2]KAHRAMAN F,KURT B,GOKMEN M.Robust face alignment for illumination and pose invariant face recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.2007:1-7.
[3]MEDIONI G,CHOI J,KUO C H,et al.Identifying noncooperative subjects at a distance using face images and inferred three dimensional face models[J].IEEE Transactionson Systems Man & Cybernetics Part A Systems & Humans,2009,39(1):12-24.
[4]LIOR W,TAL H,YANIV T.Effective unconstrained face recognition by combining multiple descriptors and learned background statistics[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(10):1978-1990.
[5]CHEN D,CAO X,WIPF D,et al.An Efficient Joint Formulation for Bayesian Face Verification[J].IEEE Transactions.on Pattern Analysis and Machine Intelligence,2017,39(1):32-46.
[6]LI P,MOHAMMED U,ELDER J,et al.Probabilistic Models for Inference about Identity[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(1):144-157.
[7]MOGHADDAM B,JEBARA T,PENTLAND A.Bayesian face recognition[J].Pattern Recognition,2000,33(11):1771-1782.
[8]CHEN D,CAO X,WEN F,et al.Blessing of Dimensionality:High-Dimensional Feature and Its Efficient Compression for Face Verification[C]//IEEE Conference on Computer Vision and Pattern Recognition.2013:3025-3032.
[9]TAN H,YANG B,MA Z.Face recognition based on the fusion of global and local HOG features of face images[J].IET Computer Vision,2014,8(3):224-234.
[10]ARASHLOO S R,KITTLER J.Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features[J].IEEE Transactions on Information Forensics and Security,2014,9(12):2100-2109.
[11]LIANG Q X,HE G H,CHEN R L,et al.Research of face recognition algorithm based on nonnegative tensorfactorization[J].Computer Science,2016,43(10):312-316.(in Chinese)
梁秋霞,何光辉,陈如丽,等.基于非负张量分解的人脸识别算法研究[J].计算机科学,2016,43(10):312-316.
[12]WENG J Y,HWANG W S.Incremental Hierarchical Discriminant Regression[J].IEEE Transactionson on Neural Networks,2007,18(2):397-415.
[13]XIONG X H,TORRE F.Supervised Descent Method and Its Application to Face Alignment[C]//IEEE Conference on Computer Vision and Pattern Recognition.2013:532-539.
[14]HUANG G B,MATTAR M,BERG T,et al.Labeled faces in the wild:a database for studying face recognition in unconstrained environments[J].International Journal of Computer Vision,2007,96(3):277-279.
[15]SIM T,BAKER S,BSAT M.The CMU pose,illumination and expression database[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(12):1615-1618.
[16]GEORGHIADESA S,BELHUMEUR P N,KRIEGMAN D J,et al.From few to many:illumination cone models for face recognition under variable lighting and pose[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(6):643-660.
[17]ZHANG K,ZHANG L,YANG M H.Real-time compressive tracking[C]//European Conference on Computer Vision.2012:864-877.
[1] LIU Dong-mei, XU Yang, WU Ze-bin, LIU Qian, SONG Bin, WEI Zhi-hui. Incremental Object Detection Method Based on Border Distance Measurement [J]. Computer Science, 2022, 49(8): 136-142.
[2] SHEN Shao-peng, MA Hong-jiang, ZHANG Zhi-heng, ZHOU Xiang-bing, ZHU Chun-man, WEN Zuo-cheng. Three-way Drift Detection for State Transition Pattern on Multivariate Time Series [J]. Computer Science, 2022, 49(4): 144-151.
[3] SANG Bin-bin, YANG Liu-zhong, CHEN Hong-mei, WANG Sheng-wu. Incremental Attribute Reduction Algorithm in Dominance-based Rough Set [J]. Computer Science, 2020, 47(8): 137-143.
[4] LIU Ling-yun, QIAN Hui, XING Hong-jie, DONG Chun-ru, ZHANG Feng. Incremental Classification Model Based on Q-learning Algorithm [J]. Computer Science, 2020, 47(8): 171-177.
[5] LI Yu, CHAI Guo-zhong, LU Chun-fu, TANG Zhi-chuan. On-line sEMG Hand Gesture Recognition Based on Incremental Adaptive Learning [J]. Computer Science, 2019, 46(4): 274-279.
[6] ZHAO Zhong-tang, ZHENG Xiao-dong. Feature Incremental Extreme Learning Machine [J]. Computer Science, 2019, 46(11A): 112-116.
[7] XIA Jun, LIU Jun-fa, JIANG Xin-long, CHEN Yi-qiang. Incremental Indoor Localization for Device Diversity Issues [J]. Computer Science, 2018, 45(10): 69-77.
[8] YAO Ming-hai, LIN Xuan-min and WANG Xian-bao. Fast Incremental Learning Algorithm of SVM with Locality Sensitive Hashing [J]. Computer Science, 2017, 44(Z11): 88-91.
[9] SUN Jing, CAI Xi-biao, JIANG Xiao-yan and SUN Fu-ming. Graph Regularized and Incremental Nonnegative Matrix Factorization with Sparseness Constraints [J]. Computer Science, 2017, 44(6): 298-305.
[10] DING Jian and WANG Shu-ying. Incremental Time Series Classification Algorithm Based on Shapelets [J]. Computer Science, 2016, 43(5): 257-260.
[11] HAO Yun-he and ZHANG Hao-feng. Incremental Learning Algorithm Based on Twin Support Vector Regression [J]. Computer Science, 2016, 43(2): 230-234.
[12] LIU Fang and LI Tian-rui. Accelerated Attribute Reduction Algorithm Based on Probabilistic Rough Sets [J]. Computer Science, 2016, 43(12): 63-70.
[13] XU Jiu-cheng, LIU Yang-yang, DU Li-na and SUN Lin. Three-way Decisions-based Incremental Learning Method for Support Vector Machine [J]. Computer Science, 2015, 42(6): 82-87.
[14] WANG Wan-liang and CAI Jing. Incremental Learning Algorithm of Non-negative Matrix Factorization with Sparseness Constraints [J]. Computer Science, 2014, 41(8): 241-244.
[15] ZHANG Yi-fan,FENG Ai-min and ZHANG Zheng-lin. Incremental Learning with Support Vector Regression [J]. Computer Science, 2014, 41(6): 166-170.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!