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 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).

Authors and Affiliations

1. Electronics and Telecommunications Research Institute, Daejon, Republic of Korea

   Seon Ho Oh & Geon-Woo Kim

Corresponding author

Correspondence toSeon Ho Oh.

Editors and Affiliations

1. Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea (Republic of)

   James J. (Jong Hyuk) Park

2. School of Computing and Information Sciences, Florida International University, Miami, Florida, USA

   Shu-Ching Chen

3. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, Adelaide, Australia

   Kim-Kwang Raymond Choo

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