User authentication in a Medical Cyber Physical Systems (MCPS) can be effectively done using biometric features. Biometric features, widely used for user authentication, are equally important to national and global technology systems. Biometric features, such as face, iris, fingerprint, are commonly used while more recently palm, vein and gait are also getting attention. To fail the traditional biometric detection systems, various spoofing approaches have also been developed over time. Among various methods, image synthesis with play-doh, gelatin, ecoflex etc. are some of the more common ways for spoofing bio-modalities. Success of traditional detection systems are related to custom tailored solutions where feature engineering for each attack type must be developed. However, this is not a feasible process when we consider countless attack possibilities. Also, a slight change in the attack can cause the whole system to be redesigned and therefore becomes a limiting constraint. The recent success of machine learning inspires this paper to explore weak and strong learners with ensemble learning approaches using AdaBoost. In essence, the paper proposes a selective ensemble fuzzy learner approach using Ada Boost, feature selection and combination of weak and strong learners to enhance the detection of bio-modality spoofing for MCPS. Our proposal was experimented on real datasets and verified on the fingerprint and iris benchmark.
|Title of host publication
|21st International Conference on Advanced Communication Technology
|Subtitle of host publication
|ICT for 4th Industrial Revolution!, ICACT 2019 - Proceeding
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 29 Apr 2019
|21st International Conference on Advanced Communication Technology, ICACT 2019 - Pyeongchang, Korea, Republic of
Duration: 17 Feb 2019 → 20 Feb 2019
|International Conference on Advanced Communication Technology, ICACT
|21st International Conference on Advanced Communication Technology, ICACT 2019
|Korea, Republic of
|17/02/19 → 20/02/19
Bibliographical noteFunding Information:
ACKNOWLEDGMENT The work was supported by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIP) (2016R1A2B4015899). Kijoon Chae is corresponding author
——————————————————————— Manuscript received on Jan. 15, 2018. This work is sponsored by Basic Science Research Program through the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIP), and a follow-up of the invited journal to the accepted & presented paper of the 19th International Conference on Advanced Communication Technology (ICACT2017), and Grant ID is 2016R1A2B4015899. Kijoon Chae is the corresponding author.
© 2019 Global IT Research Institute (GIRI).
- Biometric spoofing
- Ensemble Learning
- Feature selection
- Spoofing Detection