Abstract
Face recognition system encounters complex change that varies over time, due to a limited control over the environment. So, the facial model of an individual tends to diverse from underlying distribution that collected during initial enrollment. However, new samples that are obtained each time people try to recognize or authenticate can be used to update and refine the models. In this paper, an efficient semi-supervised learning strategy is proposed to update the face recognition model. To maintain a high performance, we exploit a probability based update approach. Performance is assessed in terms of accuracy and equal error rate (EER). Experimental results illustrate that the proposed method effectively update the classifiers.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Rischan, M., de Dharma, N.I.G., Deokjai, C.: Modeling and discovering human behavior from smartphone sensing life-log data for identification purpose. Human-centric Computing and Information Sciences 5, 31 (2015)
Yusuf, A., Mohammad, M.H.K., Athanasios, B., Sotirios, K., Nhan, N., Ruhua, J.: Designing challenge questions for location-based authentication systems: a real-life study. Hum. Centric Comput. Inf. Sci. 5, 17 (2015)
Ramon, B.-G., Norman, P., Rita, W., Raul, S.-R.: Time evolution of face recognition in accessible scenarios. Hum. Centric Comput. Inf. Sci. 5, 24 (2015)
Roli, F., Didaci, L., Marcialis, G.L.: Adaptive biometric systems that can improve with use. In: Ratha, N.K., Govindaraju, V. (eds.) Advances in Biometrics: Sensors, Algorithms and Systems, pp. 447–471. Springer, London (2008)
Zadrozny, B., Elkan, C.: Transforming Classifier Scores into Accurate Multiclass Probability Estimates. In: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 694–699. ACM, New York (2002)
Acknowledgments
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0126-16-1007, Development of Universal Authentication Platform Technology with Context-Aware Multi-Factor Authentication and Digital Signature and No. B0717-16-0107, Development of Video Crowd Sourcing Technology for Citizen Participating-Social Safety Services).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Oh, S.H., Kim, GW. (2017). Face Recognition for Mobile Self-authentication with Online Model Update. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_102
Download citation
DOI: https://doi.org/10.1007/978-981-10-5041-1_102
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5040-4
Online ISBN: 978-981-10-5041-1
eBook Packages: EngineeringEngineering (R0)