Biometric Recognition

With the development of technology, how to improve the accuracy of dorsal hand vein recognition has become the focus of current research. In order to solve this problem, this paper proposes a dorsal hand vein image recognition method which is based on multi-bit planes and Deep Learning network. The multi-bit planes can not only fully use the gray information of the images but also their intrinsic relationship between the bit planes of the images. In addition, the bit plane with less information is removed according to the Euclidean distance, and a new bit planes sequence is formed, and the accuracy of the recognition of the dorsal hand vein is improved. The algorithm is tested on the real dorsal hand vein database, and the recognition accuracy is more than 99%, which proves the effectiveness of the algorithm.

[1]  Michael Goesele,et al.  Back to the Future: Learning Shape Models from 3D CAD Data , 2010, BMVC.

[2]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Stefan Winkler,et al.  A data-driven approach to cleaning large face datasets , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[4]  Chengjun Liu,et al.  Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition , 2002, IEEE Trans. Image Process..

[5]  Cordelia Schmid,et al.  Multi-view object class detection with a 3D geometric model , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Alexander Toet,et al.  Image fusion by a ration of low-pass pyramid , 1989, Pattern Recognit. Lett..

[7]  Pascal Vincent,et al.  Dropout as data augmentation , 2015, ArXiv.

[8]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[9]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Weihong Deng,et al.  Deep Correlation Feature Learning for Face Verification in the Wild , 2017, IEEE Signal Processing Letters.

[12]  Leonidas J. Guibas,et al.  Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[13]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[14]  David Zhang,et al.  Finger-Knuckle-Print Verification Based on Band-Limited Phase-Only Correlation , 2009, CAIP.

[15]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[17]  Xiaogang Wang,et al.  Deep Learning Face Representation by Joint Identification-Verification , 2014, NIPS.

[18]  Xiangyu Zhu,et al.  High-fidelity Pose and Expression Normalization for face recognition in the wild , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Peter J. Burt,et al.  Enhanced image capture through fusion , 1993, 1993 (4th) International Conference on Computer Vision.

[20]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Miguel A. Ferrer,et al.  Improved finger-knuckle-print authentication based on orientation enhancement , 2011 .

[22]  Changyin Sun,et al.  Finger-Knuckle-Print Recognition Using LGBP , 2011, ISNN.

[23]  Ahmed Bouridane,et al.  Palmprint and Finger-Knuckle-Print for efficient person recognition based on Log-Gabor filter response , 2011 .

[24]  Phalguni Gupta,et al.  An Efficient Finger-Knuckle-Print Based Recognition System Fusing SIFT and SURF Matching Scores , 2011, ICICS.

[25]  Pramod K. Varshney Multisensor data fusion , 1997 .

[26]  Lei Zhang,et al.  Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary , 2010, ECCV.

[27]  Lei Zhang,et al.  A multi-manifold discriminant analysis method for image feature extraction , 2011, Pattern Recognit..

[28]  Oliver Rockinger,et al.  Image sequence fusion using a shift-invariant wavelet transform , 1997, Proceedings of International Conference on Image Processing.

[29]  Henry Leung,et al.  The complex backpropagation algorithm , 1991, IEEE Trans. Signal Process..

[30]  Yaonan Wang,et al.  Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images , 2002, Inf. Fusion.

[31]  Xiaogang Wang,et al.  Deeply learned face representations are sparse, selective, and robust , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Yu Qiao,et al.  A Discriminative Feature Learning Approach for Deep Face Recognition , 2016, ECCV.

[33]  Gongping Yang,et al.  A New Finger-Knuckle-Print ROI Extraction Method Based on Two-Stage Center Point Detection , 2015 .

[34]  Tieniu Tan,et al.  A Light CNN for Deep Face Representation With Noisy Labels , 2015, IEEE Transactions on Information Forensics and Security.

[35]  Song Han,et al.  Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.

[36]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Hongyu Li,et al.  Encoding local image patterns using Riesz transforms: With applications to palmprint and finger-knuckle-print recognition , 2012, Image Vis. Comput..