By Topic

Biometric cryptographic key generation based on city block distance

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Xiangqian Wu ; Biocomputing Res. Centre (BRC), Harbin Inst. of Technol., Harbin, China ; Peipei Wang ; Kuanquan Wang ; Yong Xu

Information security is becoming increasingly important in our information driven society. Cryptography is one of the most effective ways to enhance information security. Biometrics based cryptographic key generation techniques, in which biometric features are used to generate cryptographic keys, have been developed to overcome the shortages of the traditional cryptographic methods. An essential issue of biometric cryptographic key generation is to remove the variance between biometric templates of genuine users. In previous works, error correction techniques are used to eliminate these variances. However, these techniques can only be used to remove errors in Hamming metric whereas many biometric templates are real valued vectors and cannot use Hamming distance to measure the similarity, which means that the error correction techniques can not be directly used to remove the variance between these biometric templates. In this paper, we proposed a novel biometric cryptographic framework based on city block distance. In the proposed framework, the real valued biometric feature vector is firstly quantized and then encoded into a binary string in such way that the city block distance between two feature vectors is converted to Hamming distance between two binary strings. After that, the error correction techniques are used to eliminate the errors between the strings of the genuine users. Finally, the error free string is hashed to form a cryptographic key. The experimental results conducted on face and palmprint biometrics demonstrate the effectiveness of the proposed framework.

Published in:

Applications of Computer Vision (WACV), 2009 Workshop on

Date of Conference:

7-8 Dec. 2009