Fast Fingerprint Orientation Field Estimation Incorporating General Purpose GPU

Fingerprint is one of the broadly utilized biometric traits for personal identification in both civilian and forensic applications due to its high acceptability, strong security, and low cost. Fingerprint ridge orientation is one of the global fingerprint representations that keeps the holistic ridge structure in a small storage area. The importance of fingerprint ridge orientation comes from its usage in fingerprint singular point detection, coarse level classification, and fingerprint alignment. However, processing time is an important factor in any automatic fingerprint identification system, estimating that ridge orientation image may consume long processing time. This research presents an efficient ridge orientation estimation approach by incorporating a Graphics Processing Unit (GPU) capability to the traditional pixel gradient method. The simulation work shows a significant enhancement in ridge orientation estimation time by 6.41x using a general purpose GPU in comparison to the CPU execution.

[1]  Ali Ismail Awad,et al.  Evaluation of Acceleration Algorithm for Biometric Identification , 2012, NDT.

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

[3]  Sabih H. Gerez,et al.  Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Henry C. Lee,et al.  Advances in Fingerprint Technology , 1991 .

[5]  Zhang Yi,et al.  Fingerprint orientation field estimation using ridge projection , 2008, Pattern Recognit..

[6]  Sung-Bae Cho,et al.  Adaptive fingerprint image enhancement with fingerprint image quality analysis , 2006, Image Vis. Comput..

[7]  Qinzhi Zhang,et al.  FINGERPRINT ORIENTATION FIELD INTERPOLATION BASED ON THE CONSTRAINED DELAUNAY TRIANGULATION , 2007 .

[8]  Wei-Yun Yau,et al.  A review on fingerprint orientation estimation , 2011, Secur. Commun. Networks.

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

[10]  Adnan Amin,et al.  Coarse Fingerprint Registration Using Orientation Fields , 2005, EURASIP J. Adv. Signal Process..

[11]  Raghunath S. Holambe,et al.  Orientation feature for fingerprint matching , 2006, Pattern Recognit..

[12]  Erik Wynters.  Parallel processing on NVIDIA graphics processing units using CUDA , 2011 .

[13]  Andrew P. Witkin,et al.  Analyzing Oriented Patterns , 1985, IJCAI.

[14]  Manhua Liu,et al.  Invariant representation of orientation fields for fingerprint indexing , 2012, Pattern Recognit..

[15]  En Zhu,et al.  A systematic method for fingerprint ridge orientation estimation and image segmentation , 2006, Pattern Recognit..

[16]  Ali Ismail Awad,et al.  SINGULAR POINT DETECTION FOR EFFICIENT FINGERPRINT CLASSIFICATION , 2012 .

[17]  Horst Bischof,et al.  Active Fingerprint Ridge Orientation Models , 2009, ICB.

[18]  Kari Pulli,et al.  Realtime Computer Vision with OpenCV , 2012, ACM Queue.

[19]  Ali Ismail Awad,et al.  Fingerprint Local Invariant Feature Extraction on GPU with CUDA , 2013, Informatica.

[20]  José Hiroki Saito,et al.  Parallel Face Recognition Processing using Neocognitron Neural Network and GPU with CUDA High Performance Architecture , 2010 .

[21]  Khaled Ahmed Nagaty,et al.  On learning to estimate the block directional image of a fingerprint using a hierarchical neural network , 2003, Neural Networks.

[22]  Zhiyi Yang,et al.  Parallel Image Processing Based on CUDA , 2008, 2008 International Conference on Computer Science and Software Engineering.

[23]  Aboul Ella Hassanien,et al.  Impact of Some Biometric Modalities on Forensic Science , 2014, Computational Intelligence in Digital Forensics.

[24]  Xudong Jiang,et al.  Fingerprint Reference-Point Detection , 2005, EURASIP J. Adv. Signal Process..

[25]  Anil K. Jain,et al.  Fingerprint Classification Using Orientation Field Flow Curves , 2004, ICVGIP.

[26]  Anil K. Jain,et al.  Latent fingerprint enhancement via robust orientation field estimation , 2011, 2011 International Joint Conference on Biometrics (IJCB).

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

[28]  Yi Chen,et al.  Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features , 2007 .

[29]  Sharath Pankanti,et al.  Biometrics: Personal Identification in Networked Society , 2013 .

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

[31]  Ali Ismail Awad,et al.  Fingerprint Singularity Detection: A Comparative Study , 2011, ICSECS.