Non-negative locality-constrained vocabulary tree for finger vein image retrieval

Abstract

Finger vein image retrieval is a biometric identification technology that has recently attracted a lot of attention. It has the potential to reduce the search space and has attracted a considerable amount of research effort recently. It is a challenging problem owing to the large number of images in biometric databases and the lack of efficient retrieval schemes. We apply a hierarchical vocabulary tree modelbased image retrieval approach because of its good scalability and high efficiency.However, there is a large accumulative quantization error in the vocabulary tree (VT)model thatmay degrade the retrieval precision. To solve this problem, we improve the vector quantization coding in the VT model by introducing a non-negative locality-constrained constraint: the non-negative locality-constrained vocabulary tree-based image retrieval model. The proposed method can effectively improve coding performance and the discriminative power of local features. Extensive experiments on a large fused finger vein database demonstrate the superiority of our encoding method. Experimental results also show that our retrieval strategy achieves better performance than other state-of-theart methods, while maintaining low time complexity.

Keywords

non-negative locality-constrained vocabulary tree / finger vein image retrieval / large scale / inverted indexing

References

[1] Kumar A, Zhou Y. Human identification using finger images. IEEE Transactions on Image Processing, 2012, 21(4): 2228–2244

[2] Dong L, Yang G, Yin Y, Liu F, Xi X. Finger vein verification based on a personalized best patches map. In: Proceedings of IEEE International Joint Conference on Biometrics. 2014, 1–8

[3] Liu F, Yang G, Yin Y, Wang S. Singular value decomposition based minutiae matching method for finger vein recognition. Neurocomputing, 2014, 145(145): 75–89

[4] Yang G, Xi X, Yin Y. Finger vein recognition based on (2D)2 PCA and metric learning. Journal of Biomedicine and Biotechnology, 2012, 2012(3): 324249

[5] Prabhakar P, Thomas T. Finger vein identification based on minutiae feature extraction with spurious minutiae removal. In: Proceedings of the 3rd International Conference on Advances in Computing and Communications. 2013, 196–199

[6] Song W, Kim T, Kim H C, Choi J H, Kong H J, Lee S R. A finger-vein verification system using mean curvature. Pattern Recognition Letters, 2011, 32(11): 1541–1547

[7] Rosdi B, Shing C, Suandi S. Finger vein recognition using local line binary pattern. Sensors, 2011, 11(12): 11357–11371

[8] Yang G, Xi X, Yin Y. Finger vein recognition based on a personalized best bit map. Sensors, 2012, 12(2): 1738–1757

[9] Liu F, Yin Y, Yang G, Dong L, Xi X. Finger vein recognition with superpixel-based features. In: Proceedings of IEEE International Joint Conference on Biometrics. 2014, 1–8

[10] Henry E. Classification and Uses of Finger Prints. London: Routledge,1900

[11] Raghavendra R, Surbiryala J, Busch C. An efficient finger vein indexing scheme based on unsupervised clustering. In: Proceedings of IEEE International Conference on Identity, Security and Behavior Analysis. 2015, 1–8

[12] Surbiryala J, Raghavendra R, Busch C. Finger vein indexing based on binary features. In: Proceedings of IEEE Colour and Visual Computing Symposium. 2015, 1–6

[13] Zhang R, Zhang Z. A clustering based approach to efficient image retrieval. In: Proceedings of IEEE International Conference on Tools with Artificial Intelligence. 2002, 339–346

[14] Lee K, Street W. Cluster-driven refinement for content-based digital image retrieval. IEEE Transactions on Multimedia, 2004, 6(6): 817–827

[15] Tan D, Yang J, Shi Y, Xu C. A hierarchal framework for finger-vein image classification. In: Proceedings of Asian Conference on Pattern Recognition. 2013, 833–837

[16] Maltoni D, Maio D, Jain A K, Prabhakar S. Handbook of Fingerprint Recognition. Springer, 2009

[17] Tang D, Huang B, Li R, Li W. A person retrieval solution using finger vein patterns. In: Proceedings of International Conference on Pattern Recognition. 2010, 1306–1309

[18] Wang K, Yang L, Su K, Yang G, Yin Y. Binary search path of vocabulary tree based finger vein image retrieval. In: Proceedings of International Conference of Biometrics. 2016

[19] Arandjelovic R, Zisserman A. Three things everyone should know to improve object retrieval. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2012, 2911–2918

[20] Philbin J, Chum O, Isard M, Sivic J, Zisserman A. Object retrieval with large vocabularies and fast spatial matching. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2007, 1–8

[21] Yang M, Zhang D, Feng X, Zhang D. Fisher discrimination dictionary learning for sparse representation. In: Proceedings of International Conference on Computer Vision. 2011, 543–550

[22] Nister D, Stewenius H. Scalable recognition with a vocabulary tree. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2006, 2161–2168

[23] Sun Z, Zhang H, Tan T, Wang J. Iris image classification based on hierarchical visual codebook. IEEE Transactions on Software Engineering, 2014, 36(6): 1120–1133

[24] Wang J, Yang J, Yu K, Lv F, Huan g T, Gong Y. Locality-constrained linear coding for image classification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 3360–3367

[25] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401(6755): 788–791

[26] Lee D D, Seung H S. Algorithms for non-negative matrix factorization. In: Proceedings of the 13th International Conference on Neural Information Processing Systems. 2000, 535–541

[27] Xu W, Liu X, Gong Y. Document clustering based on non-negative matrix factorization. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 2003, 267–273

[28] Li Z, Liu J, Yang Y, Zhou X, Lu H. Clustering-guided sparse structural learning for unsupervised feature selection. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(9): 2138–2150

[29] Li Z, Liu J, Tang J, Lu H. Robust structured subspace learning for data representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(10): 2085–2098

[30] Chen Y, Guo X. Learning non-negative locality-constrained linear coding for human action recognition. In: Proceedings of Visual Communications and Image Processing. 2013, 1–6

[31] Hoyer P O. Non-negative sparse coding. In: Proceedings of IEEE Workshop on Neural Networks for Signal Processing. 2002, 557–565

[32] Lin T H, Kung H T. Stable and efficient representation learning with nonnegativity constraints. In: Proceedings of the 31st International Conference on Machine Learning. 2014, 1323–1331

[33] Bao C, He L, Wang Y. Linear spatial pyramid matching using nonconvex and non-negative sparse coding for image classification. In: Proceedings of IEEE China Summit and International Conference on Signal and Information Processing. 2015, 186–190

[34] Liu G, Liu Y, Guo M Z, Liu P N, Wang C Y. Non-negative localityconstrained linear coding for image classification. Acta Automatica Sinica, 2015, 41(7): 1235–1243

[35] Wang X, Yang M, Cour T, Zhu S. Contextual weighting for vocabulary tree based image retrieval. In: Proceedings of International Conference on Computer Vision. 2011, 209–216

[36] Yang L, Yang G, Yin Y, Xiao R. Sliding window-based region of interest extraction for finger vein images. Sensors, 2013, 13(3): 3799–3815

[37] Timo O, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971–987

[38] Jain R, Kasturi R, Schunck B G. Machine Vision. New York: McGraw- Hill, 1995

[39] Lowe D. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91–110

[40] Zheng L, Wang S, Liu Z, Tian Q. Lp-norm IDF for large scale image search. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2013, 1626–1633

[41] Sivic J, Zisserman A. Video google: a text retrieval approach to object matching in videos. In: Proceedings of IEEE International Conference on Computer Vision. 2003, 1470

[42] Chen D M, Tsai S S, Chandrasekhar V, Takacs G, Vedantham R, Grzeszczuk R, Girod B. Inverted index compression for scalable image matching. In: Proceedings of IEEE Data Compression Conference. 2010, 525

[43] Yin Y, Liu L, Sun X. SDUMLA-HMT: a multimodal biometric database. In: Sun Z, Lai J, Chen X, et al, eds. Biometric Recognition, Springer Berlin Heidelberg, 2011, 260–268

[44] Lu Y, Xie S J, Yoon S, Wang Z, Dong S P. An available database for the research of finger vein recognition. In: Proceedings of International Congress on Image and Signal Processing. 2013, 410–415

[45] Lu Y, Xie S J, Yoon S, Yang J, Park D S. Robust finger vein roi localization based on flexible segmentation. Sensors, 2013, 13(11): 14339–14366

[46] Asaari M, Suandi S A, Rosdi B A. Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics. Expert Systems with Applications, 2014, 41(7): 3367–3382

[47] Avrithis Y, Tolias G. Hough pyramid matching: speeded-up geometry re-ranking for large scale image retrieval. International Journal of Computer Vision, 2014, 107(1): 1–19

[48] He K, Wen F, Sun J. K-means hashing: an affinity-preserving quantization method for learning binary compact codes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2938–2945

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