A Finger Vein Recognition Algorithm Using Feature Block Fusion and Depth Neural Network

Along with the development of biometric recognition, the technology of finger vein recognition possesses better anti forgery performance and identification stability in collecting and certificating information of human bodies. The available finger vein recognition method is mainly based on template matching or whole feature recognition, suffering from light instability of the acquisition equipment which leads to low robustness. In order to strengthen the robustness, we adopt a finger vein recognition algorithm using Feature Block Fusion and Deep Belief Network (FBF-DBN), in which the nonlinear learning ability of deep neural network is used to recognize the features of finger veins. Meanwhile, we improve deep network input by using feature points set in vein images, sharply reducing the time in learning and detection, meeting the practical needs of biometric recognition specifically applied to embedded equipment. Experimental results showed that FBF-DBN algorithm presented better recognition performance and faster speed.

[1]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[2]  Cong Wang,et al.  Enhancing Attribute-Based Encryption with Attribute Hierarchy , 2009, 2009 Fourth International Conference on Communications and Networking in China.

[3]  Jinfeng Yang,et al.  An improved method for finger-vein image enhancement , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[4]  Wenxin Li,et al.  A Method of Evolving Finger Vein Template , 2012, 2012 International Symposium on Biometrics and Security Technologies.

[5]  Po Yang,et al.  Handwritten English Word Recognition Based on Convolutional Neural Networks , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[6]  Chee-Ming Ting,et al.  PCA and LDA-based face verification using back-propagation neural network , 2010, 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010).

[7]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[8]  Li Xueyan,et al.  Vein Pattern Recognitions by Moment Invariants , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[9]  Cong Wang,et al.  Efficient verifiable fuzzy keyword search over encrypted data in cloud computing , 2013, Comput. Sci. Inf. Syst..

[10]  Yann LeCun,et al.  Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[11]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[12]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[13]  Yilong Yin,et al.  Finger vein recognition with manifold learning , 2010, J. Netw. Comput. Appl..

[14]  Geoffrey E. Hinton,et al.  On deep generative models with applications to recognition , 2011, CVPR 2011.

[15]  Bin Liang,et al.  High-dimension space projection-based biometric encryption for fingerprint with fuzzy minutia , 2016, Soft Comput..

[16]  Wang Ke.  Finger Vein Recognition Based on Wavelet Moment Fused with PCA Transform , 2007 .

[17]  Hee Chan Kim,et al.  A finger-vein verification system using mean curvature , 2011, Pattern Recognit. Lett..

[18]  Jin Li,et al.  A Hybrid Cloud Approach for Secure Authorized Deduplication , 2015, IEEE Transactions on Parallel and Distributed Systems.

[19]  Cheng-Bo Yu,et al.  Finger-Vein Image Enhancement Based on Muti-Threshold Fuzzy Algorithm , 2009, 2009 2nd International Congress on Image and Signal Processing.

[20]  Wei Pi,et al.  An effective quality improvement approach for low quality finger vein image , 2010, 2010 International Conference on Electronics and Information Engineering.

[21]  G. G. Rajput,et al.  Handwritten Script Recognition Using DCT, Gabor Filter and Wavelet Features at Line Level , 2012, SOCO 2012.