Total views : 223

Latent Fingerprint Recognition using Hybridization Approach of Partial Differential Equation and Exemplar Inpainting

Affiliations

  • Computer Science and Engineering, Chandigarh Engineering College, Landran, Mohali – 140307, Punjab, India

Abstract


Objectives: Biometric based Fingerprint recognition system is one of the well-adapted approaches in online security prospects. However, due to user-friendly behaviour of advanced computing systems, biometric approach is not much in use now days. The approach left its footprints only with the applications to identify criminal activities at crime scenes where fingerprints are mainly available in latent form. Latent fingerprints are the accidently left finger skin impressions by criminals. These impressions are invisible for the naked human eye and usually captured with lasers, chemical, powders etc. These captured latent fingerprints carries less minutiae information with distorted ridges and high level of pattern overlapping. So, it is not easy to identify the criminals with partial fingerprint information. Methods/Statistical Analysis: In this paper, hybrid approach of Exemplar In painting and Partial Differential Equation is used to fill up the distorted ridges. The main goal of this work is to present the framework to reconstruct the latent distorted fingerprint and further use them to find the best match for those enhanced reconstructed latent fingerprints. For the experimentation of proposed hybrid concept, IIIT Delhi latent and NIST SD-27 databases of fingerprint are used. In this experimentation, different fingerprint enhancement filters like Canny Edge Detection Filter, Prewitt filter, Laplacian Filter, Sobel Filter, Gaussian Low Pass Filter, Gaussian High Pass Filter are used. Findings: Different filters show different performance of dataset images for latent fingerprint recognition. The overall performance of the automated latent fingerprint identification approach is analysed in terms of false acceptance rate and genuine acceptance rate. Application/Improvements: In this way, latent fingerprint can be used for the recognition of criminal activities at crime scenes where fingerprints are mainly available in latent form. The overall concept shows better results for canny filter as compare to other considered filters in enhancement.

Keywords

Binarization Approach, Criminal Activities, Exemplar Inpainting, Latent Fingerprint, Minutiae Extraction, Partial Differential Equation.

Full Text:

 |  (PDF views: 193)

References


  • Pakutharivu P, Srinath MV. A comprehensive survey on fingerprint recognition systems. Indian Journal of Science and Technology. 2015 Dec; 8(35):1-7.
  • Lakkoju PK, Shankar TN. Detection of Fingerprints in Advanced Biometric System Design. Indian Journal of Science and Technology. 2016 May; 9(17):1-6.
  • Sankaran A, Vatsa M, Singh R. Latent fingerprint matching: a survey. IEEE Access. 2014; 2:982-1004.
  • Abdullah SF, Rahman AF, Abas ZA, Saad WH. Multilayer Perceptron Neural Network in Classifying Gender using Fingerprint Global Level Features. Indian Journal of Science and Technology. 2016 Mar 15; 9(9):1-6.
  • Rahmes M, Allen JD, Elharti A, Tenali GB. Fingerprint Reconstruction Method Using Partial Differential Equation and Exemplar-Based Inpainting Methods. InBiometrics Symposium. 2007 Sep; p. 1-6.
  • Sudhaparimala S, Kodi CM, Gnanamani A, Mandal AB. Quality assessment of commercial formulations of tin based herbal drug by physico-chemical fingerprints. Indian Journal of Science and Technology. 2011 Dec; 4(12):1710-4.
  • Wolffsohn JS, Mukhopadhyay D, Rubinstein M. Image enhancement of real-time television to benefit the visually impaired. American journal of ophthalmology. 2007 Sep; 144(3):436-40.
  • Oho E, Baba N, Katoh M, Nagatani T, Osumi M, Amako K, Kanaya K. Application of the Laplacian filter to high‐resolution enhancement of SEM images. Journal of Electron Microscopy Technique. 1984 Jan; 1(4):331-40.
  • Aqrawi AA, Boe TH. Improved fault segmentation using a dip guided and modified 3D Sobel filter. In SEG Annual Meeting Society of Exploration Geophysicists. 2011.
  • Costen NP, Parker DM, Craw I. Effects of high-pass and low-pass spatial filtering on face identification. Perception & Psychophysics. 1996 Jun; 58(4):602-12.
  • Bolle RM, Senior AW, Ratha NK, Pankanti S. Fingerprint minutiae: A constructive definition. International Workshop on Biometric Authentication. 2002 Jun; p. 58-66.
  • Choi H, Boaventura M, Boaventura IA, Jain AK. Automatic segmentation of latent fingerprints. IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS). 2012 Sep; p. 303-10.
  • Zhang J, Lai R, Kuo CC. Latent fingerprint segmentation with adaptive total variation model. 2012 5th IAPR International Conference on Biometrics (ICB). 2012 Mar; p. 189-95.
  • Yoon S, Liu E, Jain AK. Springer International Publishing: On latent fingerprint image quality. Computational Forensics. 2015; p. 67-82.
  • Hicklin RA, Buscaglia J, Roberts MA. Assessing the clarity of friction ridge impressions. Forensic science international. 2013 Mar; 226(1):106-17.
  • Stephen MJ, Reddy P. Enhancing Fingerprint Image through Ridge Orientation with Neural Network Approach and Ternarization for Effective Minutiae Extraction. International Journal of Machine Learning and Computing. 2012 Aug; 2(4):397.
  • Zhao Q, Jain AK. Model based separation of overlapping latent fingerprints. IEEE Transactions on Information Forensics and Security. 2012 Jun; 7(3):904-18.
  • Feng J, Zhou J, Jain AK. Orientation field estimation for latent fingerprint enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013 Apr; 35(4):925-40.
  • Cao K, Liu E, Jain AK. Segmentation and enhancement of latent fingerprints: A coarse to fine ridge structure dictionary. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2014 Sep; 36(9):1847-59.
  • Liu M, Chen X, Wang X. Latent fingerprint enhancement via multi-scale patch based sparse representation. IEEE Transactions on Information Forensics and Security. 2015 Jan; 10(1):6-15.
  • Paulino AA, Feng J, Jain AK. Latent fingerprint matching using descriptor-based hough transform. IEEE Transactions on Information Forensics and Security. 2013 Jan; 8(1):31-45.
  • Sankaran A, Pandey P, Vatsa M, Singh R. On latent fingerprint minutiae extraction using stacked denoising sparse autoencoders. InBiometrics (IJCB). 2014 Sep 29; p. 1-7.
  • Jain AK, Feng J. Latent fingerprint matching. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011 Jan; 33(1):88-100.
  • Jain AK, Feng J, Nagar A, Nandakumar K. On matching latent fingerprints. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008, CVPRW’08. 2008 Jun; p. 1-8.
  • Paulino AA, Jain AK, Feng J. Latent fingerprint matching: Fusion of manually marked and derived minutiae. 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images. 2010 Aug; p. 63-70.

Refbacks

  • »
  • »
  • »
  • »
  • »


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.