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User-centric incremental learning model of dynamic personal identification for mobile devices

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Abstract

This study presents a user-centric incremental learning model based on the proposed output selection strategy (OSS) and multiview body direction estimation for dynamic personal identification systems on mobile devices. First, the OSS filters primitive results generated from the classifier, so that the refined information can be used to update the learning model. Second, the robustness of the model is enhanced by using different views of faces as system input, which allows the learning model to adapt itself when either of facial views is not available. In addition, the body direction estimation method is proposed for estimating multiple views of a person by matching templates of human shapes and skin colors. An experiment on 168,000 test samples (20 classes with three facial views) is conducted to evaluate the proposed system. The experimental results show that the proposed method improves accuracy by more than 40 % compared to baseline, and correspondingly confirms the effectiveness of the proposed idea.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan

    Hsin-Chun Tsai, Bo-Wei Chen, Jhing-Fa Wang & Hung-Chieh Tai

  2. Department of Electrical Engineering, Feng Chia University, Taichung, Taiwan

    Karunanithi Bharanitharan

  3. School of Computer Science and Engineering, Kyungpook National University, Daegu, South Korea

    Anand Paul

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  1. Hsin-Chun Tsai
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  2. Bo-Wei Chen
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  3. Karunanithi Bharanitharan
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  4. Anand Paul
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  5. Jhing-Fa Wang
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  6. Hung-Chieh Tai
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Correspondence to Hsin-Chun Tsai.

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Tsai, HC., Chen, BW., Bharanitharan, K. et al. User-centric incremental learning model of dynamic personal identification for mobile devices. Multimedia Systems 21, 121–130 (2015). https://doi.org/10.1007/s00530-013-0328-y

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