基于子空间与纹理特征融合的掌纹识别
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摘要
针对单一描述符无法准确获取有效掌纹特征导致识别率低的问题,提出一种基于子空间与纹理特征融合的掌纹识别方法。利用稳健线性判别分析和局部方向二值模式分别获取掌纹图像的子空间特征和纹理特征;基于加权串联方法实现子空间特征与纹理特征的有效融合;根据融合特征向量间的卡方距离进行匹配识别。在PolyU图库和自建非接触图库上的实验结果表明,识别时间分别为0.3069s和0.3127s,最低等误率分别为0.3440%和1.4922%;与其他方法相比,所提方法在保证实时性的前提下,能够准确提取掌纹图像的有效特征信息,提高系统识别性能。
Aiming at the problem of low recognition rate because the single descriptor cannot accurately obtain the effective palmprint features,apalmprint recognition method is proposed based on subspace and texture feature fusion.The subspace feature and texture feature of a palmprint image are obtained by robust linear discriminant analysis and local direction binary pattern,respectively.The weighted concatenation method is used for the subspace and texture feature fusion.The chi-square distance among the fused feature vectors is used for identification matching.The experimental results on the PolyU and the self-built non-contact databases show that the recognition time is 0.3069 sand 0.3127 s,respectively,and the lowest equal error rate is only 0.3440% and 1.4922%,respectively.Compared with other methods,the proposed method can accurately obtain the effective feature information of a palmprint image and improve the system recognition performance under the premise that the realtime performance is ensured.
Aiming at the problem of low recognition rate because the single descriptor cannot accurately obtain the effective palmprint features,apalmprint recognition method is proposed based on subspace and texture feature fusion.The subspace feature and texture feature of a palmprint image are obtained by robust linear discriminant analysis and local direction binary pattern,respectively.The weighted concatenation method is used for the subspace and texture feature fusion.The chi-square distance among the fused feature vectors is used for identification matching.The experimental results on the PolyU and the self-built non-contact databases show that the recognition time is 0.3069 sand 0.3127 s,respectively,and the lowest equal error rate is only 0.3440% and 1.4922%,respectively.Compared with other methods,the proposed method can accurately obtain the effective feature information of a palmprint image and improve the system recognition performance under the premise that the realtime performance is ensured.
引文
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[14]Fang X Z,Teng S H,Lai Z H,et al.Robust latent subspace learning for image classification[J].IEEETransactions on Neural Networks and Learning Systems,2018,29(6):2502-2515.
[15]Zou H,Hastie T,Tibshirani R.Sparse principal component analysis[J].Journal of Computational and Graphical Statistics,2006,15(2):265-286.
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[18]Luo Y,Zhang T,Zhang Y.A novel fusion method of PCA and LDP for facial expression feature extraction[J].Optik,2016,127(2):718-721.
[19]Lin S,Xu T Y,Wang Y.Palm vein recognition based on Gabor wavelet and NBP algorithm[J].Laser&Optoelectronics Progress,2017,54(5):051002.林森,徐天扬,王颖.基于Gabor小波和NBP算法的手掌静脉识别[J].激光与光电子学进展,2017,54(5):051002.
[2]Yue F,Zuo W M,Zhang D P.Survey of palmprint recognition algorithms[J].Acta Automatica Sinica,2010,36(3):353-365.岳峰,左旺孟,张大鹏.掌纹识别算法综述[J].自动化学报,2010,36(3):353-365.
[3]Zhang D,Kong W K,You J,et al.Online palmprint identification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(9):1041-1050.
[4]Kwak N,Choi C H.Input feature selection for classification problems[J].IEEE Transactions on Neural Networks,2002,13(1):143-159.
[5]Liu Y,Tang F,Zeng Z Y.Feature selection based on dependency margin[J].IEEE Transactions on Cybernetics,2015,45(6):1209-1221.
[6]Zhang Z,Shao L,Xu Y,et al.Marginal representation learning with graph structure selfadaptation[J].IEEE Transactions on Neural Networks and Learning Systems,2018,29(10):4645-4659.
[7]Wang H,Kang W X,Chen X P.Palm print and palm vein joint recognition system based video[J].Acta Optica Sinica,2018,38(2):0215004.王浩,康文雄,陈晓鹏.基于视频的掌纹掌脉联合识别系统[J].光学学报,2018,38(2):0215004.
[8]Li X C,Zhang C H,Lin S.Palmprint and palm vein feature fusion recognition based on BSLDP and canonical correlation analysis[J].Laser&Optoelectronics Progress,2018,55(5):051012.李新春,张春华,林森.基于BSLDP和典型相关分析的掌纹掌脉融合识别[J].激光与光电子学进展,2018,55(5):051012.
[9]Ali M M H,Yannawar P L,Gaikwad A T.Multialgorithm of palmprint recognition system based on fusion of local binary pattern and two-dimensional locality preserving projection[J].Procedia Computer Science,2017,115:482-492.
[10]Tabejamaat M,Mousavi A.Generalized Gabor filters for palmprint recognition[J].Pattern Analysis and Applications,2018,21(1):261-275.
[11]Zhang S W,Wang H X,Huang W Z,et al.Combining modified LBP and weighted SRC for palmprint recognition[J].Signal,Image and Video Processing,2018,12(6):1035-1042.
[12]Lai Z H,Xu Y,Jin Z,et al.Human gait recognition via sparse discriminant projection learning[J].IEEETransactions on Circuits and Systems for Video Technology,2014,24(10):1651-1662.
[13]Fei L K,Xu Y,Teng S H,et al.Local orientation binary pattern with use for palmprint recognition[M].Cham:Springer International Publishing,2017:213-220.
[14]Fang X Z,Teng S H,Lai Z H,et al.Robust latent subspace learning for image classification[J].IEEETransactions on Neural Networks and Learning Systems,2018,29(6):2502-2515.
[15]Zou H,Hastie T,Tibshirani R.Sparse principal component analysis[J].Journal of Computational and Graphical Statistics,2006,15(2):265-286.
[16]Boyd S,Parikh N,Chu E,et al.Distributed optimization and statistical learning via the alternating direction method of multipliers[J].Foundations and Trends?in Machine Learning,2010,3(1):1-122.
[17]Candès E J,Li X,Ma Y,et al.Robust principal component analysis?[J].Journal of the ACM,2011,58(3):1-37.
[18]Luo Y,Zhang T,Zhang Y.A novel fusion method of PCA and LDP for facial expression feature extraction[J].Optik,2016,127(2):718-721.
[19]Lin S,Xu T Y,Wang Y.Palm vein recognition based on Gabor wavelet and NBP algorithm[J].Laser&Optoelectronics Progress,2017,54(5):051002.林森,徐天扬,王颖.基于Gabor小波和NBP算法的手掌静脉识别[J].激光与光电子学进展,2017,54(5):051002.