Personal Identification using Contactless Live 3D Finger Scans

  • Presenter: Ajay Kumar, The Hong Kong Polytechnic University, Hong Kong
  • Length: Half-day
  • Abstract: Automated fingerprint identification is commonly used for the civilian and law-enforcement applications around the world. Traditional acquisition of fingerprint scans by pressing or rolling of finger against the hard surface that often results in partial or degraded images due to improper finger placement, skin deformation, slippages, smearing or due to sensor noise. As a result available potential from the fingerprints is not realized. Therefore, contactless 3D finger imaging systems have emerged to provide ideal solutions to above intrinsic problems. Such 3D approaches can also provide more accurate personal identification as rich information is available from 3D fingerprint images. Emerging solutions for the contactless 3D fingerprint acquisition are largely based on shape from silhouette, structured lighting or photometric stereo based imaging. However widely accepted standards or the representation of 3D fingerprint features is yet to emerge. The minutiae features are widely considered to be most reliable and widely employed by law enforcement experts and commercial 2D fingerprint systems available today. Accurate recovery, representation, selection, registration and matching of 3D fingerprints is essentially a biometrics recovery/alignment, and matching problem. This half-day tutorial will provide complete overview and algorithmic details relating to 3D fingerprint matching. The course contents for this tutorial are will be organized as follows:
    1. Introduction to Contactless Fingerprint Identification
    2. Contactless Live 3D Fingerprint Imaging
      1. Structured Lighting
      2. Multiple Cameras
      3. Photometric Stereo
    3. Preprocessing 3D Fingerprint Data
    4. Recovering 3D Minutiae (also other features)
    5. Matching two 3D Fingerprint templates
      1. Registering two 3D fingerprints
      2. Selection/Evaluation of 3D Minutiae
      3. Generating Matching Score
    6. Individuality of 3D Fingerprints
    7. Matching 3D Fingerprints with Legacy Fingerprint Databases
    8. Conclusions and Further Work
      1. Sharing of 3D Fingerprint Database and Codes
      2. Theoretical Problems
      3. A List of Unanswered Questions
  • Relevant References:
    1. Ajay Kumar and Cyril Kwong, “Towards contactless, low-cost and accurate 3D fingerprint identification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, pp. 681-696, March 2015.
    2. Ajay Kumar and Cyril Kowng, “Contactless 3D biometric feature identification and method thereof,” US Patent No. 8, 953 854, Feb. 2015.
    3. Ajay Kumar and Cyril Kwong, “Towards contactless, low cost and accurate 3D fingerprint identification,” Proc. CVPR 2013, Portland, pp. 3438-3443, June 2013.
    4. Ajay Kumar and Yingbo Zhou, “Human identification using finger images,” IEEE Transactions on Image Processing, vol. 21, pp. 2228-2244, Apr. 2012.
    5. Anil K. Jain and Ajay Kumar, “Biometrics of next generation: an overview,” Second Generation Biometrics: The Ethical, Legal and Social Context, E. Mordini and D. Tzovaras (Eds.), Springer, 2012.
    6. Ajay Kumar and Yingbo Zhou, “Contactless fingerprint identification using level zero features,” Proc. CVPR 2011, pp. 121-126, Colorado Springs, CVPRW’11, June 2011.
    7. Yingbo Zhou and Ajay Kumar, “Human identification using palm-vein images,” IEEE Transactions on Information Forensics and Security, vol. 6, no. 4, pp. 1259-1274, Dec. 2011.
    8. Vivek Kanhangad, Ajay Kumar, and David Zhang, “A unified framework for contactless hand identification,” IEEE Transactions on Image Processing, vol. 6, no. 3, pp. 1014-1027, Sep. 2011.
    9. Vivek Kanhangad, Ajay Kumar, and David Zhang, “Contactless and pose invariant biometric identification using hand surface,” IEEE Transactions on Information Forensics and Security, vol. 20, no. 5, pp. 1415-1424, May 2011.
    10. Ajay Kumar, Yingbo Zhou, “Method and apparatus for personal identification using finger imaging,” US Patent No. 20110304720, 2011.
    11. Y. Wang, L. G. Hassebrook, and D. L Lau, “Data acquisition and processing of 3-D fingerprints,” IEEE Trans. Info.Forensics and Security, vol. 5, no. 4, pp. 750-759, Dec. 2010.
    12. G. Parziale and Y. Chen, “Advanced technologies for touchless fingerprint recognition,” in Handbook of Remote Biometrics, Advances in Pattern Recognition, M. Tistarelli, S. Z. Li, and R. Chellapa. (Eds.), Springer 2009.

Continuous User Authentication on Mobile Devices

  • Presenter: Vishal M. Patel, Rutgers University, USA
  • Length: Half-day
  • Abstract: Recent developments in sensing and communication technologies have led to an explosion in the use of mobile devices such as smartphones and tablets. With the increase in use of mobile devices, one has to constantly worry about the security and privacy as the loss of a mobile device would compromise personal information of the user. To deal with this problem, continuous authentication (also known as active authentication) systems have been proposed in which users are continuously monitored after the initial access to the mobile device. This tutorial will provide an overview of different continuous authentication methods on mobile devices. We will discuss merits and drawbacks of available approaches and identify promising avenues of research in this rapidly evolving field. The tutorial should prove valuable to security and biometrics experts, exposing them to opportunities provided by continuous authentication approaches. It should also prove beneficial to experts in computer vision and signal processing, introducing them to a different tool with very interesting research problems.