A novel method based on deep learning for aligned fingerprints matching

In this study, a novel method based on deep learning for aligned fingerprints matching is proposed. According to the characteristics of fingerprint images, a convolutional network, Finger ConvNet, is designed. In addition, a new joint supervision signal is used to train Finger ConvNet to obtain deep features. Experimental studies are performed on public fingerprint datasets, the ID Card fingerprint dataset and the Ten-Finger Fingerprint Card fingerprint dataset. Furthermore, four performance indicators, the false matching rate (FMR), false non-matching rate (FNMR), equal error rate (EER) and receiver operating characteristic (ROC) curve, are measured. The experimental results demonstrate the effectiveness of the proposed method, which achieved a competitive effect in comparison with conventional fingerprint matching algorithms in fingerprint verification tasks using the FVC2000, FVC2002, and FVC2004 datasets. Moreover, the matching speed of the proposed method was almost 5 times faster than the fastest conventional fingerprint matching algorithms. In addition, it can be used as a fast matching method to filter out many templates with low scores by setting a threshold according to the matching scores and thus accelerate the process in identification tasks.

[1]  Ravinder Kumar,et al.  A Robust Fingerprint Matching System Using Orientation Features , 2016, J. Inf. Process. Syst..

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

[3]  Mikel Galar,et al.  Minutiae filtering to improve both efficacy and efficiency of fingerprint matching algorithms , 2014, Eng. Appl. Artif. Intell..

[4]  Tetsuya Ogata,et al.  Audio-visual speech recognition using deep learning , 2014, Applied Intelligence.

[5]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[6]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals , 2018, Applied Intelligence.

[7]  Ouajdi Korbaa,et al.  Fast and Accurate Fingerprint Matching Using Expanded Delaunay Triangulation , 2017, 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA).

[8]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[9]  Hong Chen,et al.  Fingerprint matching based on global comprehensive similarity , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Anil K. Jain,et al.  Fingerprint Reconstruction: From Minutiae to Phase , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Yonghong Liu,et al.  A Method for Singular Points Detection Based on Faster-RCNN , 2018 .

[12]  Jiann-Der Lee,et al.  Fingerprint classification based on decision tree from singular points and orientation field , 2014, Expert Syst. Appl..

[13]  Bipin Kumar Tripathi,et al.  On the complex domain deep machine learning for face recognition , 2017, Applied Intelligence.

[14]  David J. Fleet,et al.  Hamming Distance Metric Learning , 2012, NIPS.

[15]  Anil K. Jain,et al.  On-line fingerprint verification , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[16]  Humberto Bustince,et al.  A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal , 2015, Knowl. Based Syst..

[17]  Anil K. Jain,et al.  Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge , 2017, 2018 International Conference on Biometrics (ICB).

[18]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[19]  Dario Maio,et al.  Synthetic fingerprint-image generation , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[20]  Francisco Herrera,et al.  A survey of fingerprint classification Part I: Taxonomies on feature extraction methods and learning models , 2015, Knowl. Based Syst..

[21]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Davide Maltoni,et al.  Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Hamido Fujita,et al.  Computer Aided detection for fibrillations and flutters using deep convolutional neural network , 2019, Inf. Sci..

[24]  Xiaogang Wang,et al.  DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Sepp Hochreiter,et al.  Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.

[26]  Jie Tian,et al.  A minutiae matching algorithm in fingerprint verification , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[27]  Congying Han,et al.  Partial Fingerprint Matching via Phase-Only Correlation and Deep Convolutional Neural Network , 2017, ICONIP.

[28]  Qiongxiu Li,et al.  Multi-feature Score Fusion for Fingerprint Recognition based on Neighbor Minutiae Boost , 2017 .

[29]  Bin Li,et al.  Link prediction based on sampling in complex networks , 2017, Applied Intelligence.

[30]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[31]  Congying Han,et al.  A novel fingerprint classification method based on deep learning , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[32]  Francisco Herrera,et al.  A survey of fingerprint classification Part II , 2015 .

[33]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[34]  Xudong Jiang,et al.  Fingerprint minutiae matching based on the local and global structures , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[35]  Anil K. Jain,et al.  FVC2000: Fingerprint Verification Competition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[37]  Arun Ross,et al.  Fingerprint Matching Using Feature Space Correlation , 2002, Biometric Authentication.

[38]  Anil K. Jain,et al.  FVC2004: Third Fingerprint Verification Competition , 2004, ICBA.

[39]  Anil K. Jain,et al.  Fingerprint Image Enhancement: Algorithm and Performance Evaluation , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Yann LeCun,et al.  Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[41]  Anil K. Jain,et al.  FVC2002: Second Fingerprint Verification Competition , 2002, Object recognition supported by user interaction for service robots.

[42]  Liang Gao,et al.  Deep learning-based personality recognition from text posts of online social networks , 2018, Applied Intelligence.

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

[44]  S. H. Gerez,et al.  A correlation-based fingerprint verification system , 2000 .

[45]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

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

[47]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition , 2005, Springer Professional Computing.

[48]  Davide Maltoni,et al.  Fingerprint Indexing Based on Minutia Cylinder-Code , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  Dario Maio,et al.  Synthetic fingerprint-database generation , 2002, Object recognition supported by user interaction for service robots.

[50]  Salih Gorgunoglu,et al.  A Robust Correlation Based Fingerprint Matching Algorithm for Verification , 2007 .

[51]  Sung-Bae Cho,et al.  Cancer classification using ensemble of neural networks with multiple significant gene subsets , 2007, Applied Intelligence.