Realtime training on mobile devices for face recognition applications

Kwontaeg Choi, Kar Ann Toh, Hyeran Byun

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)


Due to the increases in processing power and storage capacity of mobile devices over the years, an incorporation of realtime face recognition to mobile devices is no longer unattainable. However, the possibility of the realtime learning of a large number of samples within mobile devices must be established. In this paper, we attempt to establish this possibility by presenting a realtime training algorithm in mobile devices for face recognition related applications. This is differentiated from those traditional algorithms which focused on realtime classification. In order to solve the challenging realtime issue in mobile devices, we extract local face features using some local random bases and then a sequential neural network is trained incrementally with these features. We demonstrate the effectiveness of the proposed algorithm and the feasibility of its application in mobile devices through empirical experiments. Our results show that the proposed algorithm significantly outperforms several popular face recognition methods with a dramatic reduction in computational speed. Moreover, only the proposed method shows the ability to train additional samples incrementally in realtime without memory failure and accuracy degradation using a recent mobile phone model.

Original languageEnglish
Pages (from-to)386-400
Number of pages15
JournalPattern Recognition
Issue number2
Publication statusPublished - 2011 Feb

Bibliographical note

Funding Information:
This research was supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2010-C1090-1021-0008). This work was supported by the National Research Foundation of Korea(NRF) through the Biometrics Engineering Research Center(BERC) at Yonsei University (R112002105070030(2010)).

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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