A Method for Singular Points Detection Based on Faster-RCNN

Most methods for singular points detection usually depend on the orientation fields of fingerprints, which cannot achieve reliable and accurate detection of poor quality fingerprints. In this study, a new method for fingerprint singular points detection based on Faster-RCNN (Faster Region-based Convolutional Network method) is proposed, which is a two-step process, and an orientation constraint is added in Faster-RCNN to obtain orientation information of singular points. Besides, we designed a convolutional neural network (ConvNet) for singular points detection according to the characteristics of fingerprint images and the existing works. Specifically, the proposed method could extract singular points directly from raw fingerprint images without traditional preprocessing. Experimental results demonstrate the effectiveness of the proposed method. In comparison with other detection algorithms, our method achieves 96.03% detection rate for core points and 98.33% detection rate for delta points on FVC2002 DB1 dataset while 90.75% for core points and 94.87% on NIST SD4 dataset, which outperform other algorithms.

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

[2]  Fanglin Chen,et al.  A Novel Algorithm for Detecting Singular Points from Fingerprint Images , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[4]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

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

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

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

[8]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Yichen Wei,et al.  Deep Feature Flow for Video Recognition , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Bhiksha Raj,et al.  SphereFace: Deep Hypersphere Embedding for Face Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[13]  Lingling Fan,et al.  Singular Points Detection Based on Zero-Pole Model in Fingerprint Images , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[15]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[17]  Congying Han,et al.  Multi-scaling Detection of Singular Points Based on Fully Convolutional Networks in Fingerprint Images , 2017, CCBR.

[18]  Nalini K. Ratha,et al.  Impact of singular point detection on fingerprint matching performance , 2005, Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05).

[19]  Akio Tojo,et al.  Fingerprint pattern classification , 1984, Pattern Recognit..

[20]  Aurelio Uncini,et al.  Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning , 2018, Applied Sciences.

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

[22]  Vincenzo Piuri,et al.  A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks , 2017, Pattern Recognit. Lett..

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