Qinghai Gao

Work place: Department of Security Systems & LET, Farmingdale State College, 2350 Broadhollow Road, Farmingdale, NY 11735

E-mail: gaoqj@farmingdale.edu

Website: https://www.farmingdale.edu/faculty/?fid=217

Research Interests: Network Security, Information Security, Biometrics, Bioinformatics, Cryptography


Qinghai Gao, born in Shandong China in 1969, received a Ph.D. in computer science from the City University of New York in 2007.

Currently, he is an Associate Professor in the Department of Security Systems & Law Enforcement Technology at Farmingdale State College. Before joining Farmingdale, he taught full-time in the China University of Petroleum for a few years. From 1998 to 2007 he taught as Adjuncts in Brooklyn College, Lehman College, NYC College of Technology, College of Staten Island, and York College. Since 2001 he held various positions in IT industry as Software Developer, Database Administrator, Network Engineer, Researcher, Consultant, and Information Security Specialist. He has extensive experience with fingerprint identification, computer security, and cryptography. He has published one book and numerous articles. His present research interests include Fingerprint Identification, Digital Forensics, Computer Security, Biometrics, Cryptography, and Bioinformatics.

Dr. Gao is a current member of following professional organizations: International Association of Identification (IAI), Association of Computing Machinery (ACM), International Association of Computer Investigative Specialists (IACIS), and High Technology Crime Investigation Association (HTCIA).

Author Articles
Toward Constructing Cancellable Templates using K-Nearest Neighbour Method

By Qinghai Gao

DOI: https://doi.org/10.5815/ijcnis.2017.05.01, Pub. Date: 8 May 2017

The privacy of biometric data needs to be protected. Cancellable biometrics is proposed as an effective mechanism of protecting biometric data. In this paper a novel scheme of constructing cancellable fingerprint minutiae template is proposed. Specifically, each real minutia point from an original template is mapped to a neighbouring fake minutia in a user-specific randomly generated synthetic template using the k-nearest neighbour method. The recognition template is constructed by collecting the neighbouring fake minutiae of the real minutiae. This scheme has two advantages: (1) An attacker needs to capture both the original template and the synthetic template in order to construct the recognition template; (2) A compromised recognition template can be cancelled easily by replacing the synthetic template. Single-neighboured experiments of self-matching, nonself-matching, and imposter matching are carried out on three databases: DB1B from FVC00, DB1B from FVC02, and DB1 from FVC04. Double-neighboured tests are also conducted for DB1B from FVC02. The results show that the constructed recognition templates can perform more accurately than the original templates and it is feasible to construct cancellable fingerprint templates with the proposed approach.

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A Preliminary Study of Fake Fingerprints

By Qinghai Gao

DOI: https://doi.org/10.5815/ijcnis.2014.12.01, Pub. Date: 8 Nov. 2014

Fingerprint is a widely used biometrics. Its extensive usage motivates imposter to fabricate fake fingerprints. Vitality detection has been proposed to prevent counterfeit finger attack. Currently the detection can be done either during the process of acquiring fingerprint image or by comparing multiple sequentially acquired images. It is an ongoing research problem to detect whether a given fingerprint image is obtained from a real or a fake fingertip. In this paper we look into the differences between real and fake fingerprints as the first step to approach this problem. Specifically, we study the effects of different imaging sensors on the sizes of templates and on the matching scores between real and fake fingerprints. We also compare the fake fingerprints made from different materials. Experiments are carried out with two publicly available fingerprint databases and the findings are reported.

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