基于广义典型相关分析融合和鲁棒概率协同表示的人脸指纹多模态识别
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摘要
针对单模态生物特征识别容易受自身条件和环境变化的影响,鉴于人脸识别和指纹识别已经在生物识别系统中得到了广泛应用,提出了二者特征信息融合的多模态生物特征识别方法。该方法首先对人脸、指纹图像进行预处理,并对这两种模态均提取LBP和Gabor特征,然后将广义典型相关分析方法分别引入到人脸多特征融合和指纹多特征融合中,应用分块对角矩阵组合上述融合的人脸特征和指纹特征,最后用鲁棒概率协同表示分类器进行分类。在两个多模态数据库上的实验结果表明:与人脸或指纹单模态生物特征识别相比,基于人脸指纹的多模态生物特征识别具有更高的识别率和更好的稳定性;所提出的基于广义典型相关分析的特征融合方法优于传统的融合方法。
Unimodal biometrics is easily affected by conditions and environmental variations. Since face recognition and fingerprint recognition have been widely used in biometric systems, a multimodal biometrics method was presented based on the fusion of face and fingerprint features. The image processing methods were applied to the face and fingerprint images, and both LBP and Gabor features were extracted for these two biological features. Then, a generalized canonical correlation analysis was introduced into the multi-feature fusion of faces and multi-feature fusion of fingerprints respectively. A block diagonal matrix was taken to combine the above fused face features and fingerprint features.Finally, a classification was executed by solving the robust probabilistic collaborative representation.The experimental results on two multimodal databases demonstrate that the multimodal biometrics of faces and fingerprints has higher accuracy and better stability than the unimodal biometrics. The proposed fusion method based on the generalized canonical correlation analysis is superior to traditional methods.
Unimodal biometrics is easily affected by conditions and environmental variations. Since face recognition and fingerprint recognition have been widely used in biometric systems, a multimodal biometrics method was presented based on the fusion of face and fingerprint features. The image processing methods were applied to the face and fingerprint images, and both LBP and Gabor features were extracted for these two biological features. Then, a generalized canonical correlation analysis was introduced into the multi-feature fusion of faces and multi-feature fusion of fingerprints respectively. A block diagonal matrix was taken to combine the above fused face features and fingerprint features.Finally, a classification was executed by solving the robust probabilistic collaborative representation.The experimental results on two multimodal databases demonstrate that the multimodal biometrics of faces and fingerprints has higher accuracy and better stability than the unimodal biometrics. The proposed fusion method based on the generalized canonical correlation analysis is superior to traditional methods.
引文
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[2]CHEN Y R,YANG J C,WANG C,et al.Multimodal biometrics recognition based on local fusion visual features and variational Bayesian extreme learning machine[J].Expert Systems with Applications,2016,64:93-103.
[3]王小雪,杨会成.融合指纹和人脸的生物特征身份认证方法[J].重庆理工大学学报:自然科学版,2013,27(7):67-70.
[4]FAUNDEZ-ZANUY M.Data fusion in biometrics[J].IEEE Aerospace and Electronic Systems Magazine,2005,20(1):34-38.
[5]YANG J F,ZHANG X.Feature-level fusion of fingerprint and finger-vein for personal identification[J].Pattern Recognition Letters,2012,33(5):623-628.
[6]LIU C J,WECHSLER H.A shape-and texture-based enhanced Fisher classifier for face recognition[J].IEEETransactions on Image Processing,2001,10(4):598-608.
[7]YANG J,YANG J Y.Generalized K-L transform based combined feature extraction[J].Pattern Recognition,2002,35(1):295-297.
[8]HUANG Z X,LIU Y X,LI C,et al.A robust face and ear based multimodal biometric system using sparse representation[J].Pattern Recognition,2013,46(8):2156-2168.
[9]WRIGHT J,YANG A Y,GANESH A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227.
[10]DONOHO D L,TSAIG Y.Fast solution of l1 norm minimization problems when the solution may be sparse[J].IEEE Transactions on Information Theory,2008,54(11):4789-4812.
[11]ZHANG L,YANG M,FENG X C.Sparse representation or collaborative representation:which helps face recognition?[C]//Proceedings of 2011 IEEE International Conference on Computer Vision.Barcelona:IEEE,2011:471-478.
[12]CHEN Y C,PATEL V M,SHEKHAR S,et al.Videobased face recognition via joint sparse representation[C]//Proceedings of the 2013 10th IEEEInternational Conference and Workshops on Automatic Face and Gesture Recognition.Shanghai:IEEE,2013:1-8.
[13]MA L,WANG C H,XIAO B H,et al.Sparse representation for face recognition based on discriminative low-rank dictionary learning[C]//Proceedings of 2012IEEE Conference on Computer Vision and Pattern Recognition.RI:IEEE,2012:2586-2593.
[14]DHARAVATH K,TALUKDAR F A,LASKAR R H.Improving face recognition rate with image preprocessing[J].Indian Journal of Science and Technology,2014,7(8):1170-1175.
[15]REISFELD D,YESHURUN Y.Preprocessing of face images:Detection of features and pose normalization[J].Computer Vision and Image Understanding,1998,71(3):413-430.
[16]刘灵丽.指纹图像预处理与特征提取[D].长沙:湖南大学,2005.
[17]MENG J,GAO Y,WANG X K,et al.Face recognition based on local binary patterns with threshold[C]//Proceedings of 2010 IEEE International Conference on Granular Computing.San Jose:IEEE,2010:352-356.
[18]YANG M,ZHANG L.Gabor feature based sparse representation for face recognition with gabor occlusion dictionary[M]//DANIILIDIS K,MARAGOS P,PARAGIOS N.Computer Vision-ECCV 2010,Heidelberg:Springer,2010:448-461.
[19]BRO R,SMILDE A K.Principal component analysis[J].Analytical Methods,2014,6(9):2812-2831.
[20]ABDI H,GUILLEMOT V,ESLAMI A,et al.Canonical correlation analysis[M]//ALHAJJ R,ROKNE J.Encyclopedia of Social Network Analysis and Mining,New York:Springer,2017:1-16.
[21]CAI S J,ZHANG L,ZUO W M,et al.A probabilistic collaborative representation based approach for pattern classification[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:2950-2959.
[22]GUMUS E,KILIC N,SERTBAS A,et al.Evaluation of face recognition techniques using PCA,wavelets and SVM[J].Expert Systems with Applications,2010,37(9):6404-6408.
[23]SAGONAS C,ANTONAKOS E,TZIMIROPOULOS G,et al.300 faces in-the-wild challenge:database and results[J].Image and Vision Computing,2016,47:3-18.
[24]CAPPELLI R,FERRARA M,MALTONI D.Fingerprint indexing based on minutia cylinder-code[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2011,33(5):1051-1057.
[25]MANDAL B.Face recognition:perspectives from the real world[C]//Proceedings of 2016 14th International Conference on Control,Automation,Robotics and Vision.Phuket:IEEE,2016:1-5.