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Hand number gesture recognition using recognized hand parts in depth images

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Abstract

In this paper, we present a novel approach of recognizing hand number gestures using the recognized hand parts in a depth image. Our proposed approach is divided into two stages: (i) hand parts recognition by random forests (RFs) and (ii) rule-based hand number gestures recognition. In the first stage, we create a database (DB) of synthetic hand depth silhouettes and their corresponding hand parts-labeled maps and then train RFs with the DB. Via the trained RFs, we recognize or label the hand parts in a depth silhouette. In the second stage, based on the information of the recognized or labeled hand parts, hand number gestures are recognized according to our derived rules. In our experiments, we quantitatively and qualitatively evaluated our hand parts recognition system with synthetic and real data. Then, we tested our hand number gesture recognition system with real data. Our results show the average recognition rate of 97.80 % over the ten hand number gestures from five different subjects.

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Acknowledgments

This research was supported by the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency (NIPA-2013-(H0301-13-2001)). This work was also supported by the Industrial Strategic Technology Development Program (10035348, Development of a Cognitive Planning and Learning Model for Mobile Platforms) funded by the Ministry of Knowledge Economy(MKE, Korea) and the Industrial Core Technology Development Program (10049079, Development of Mining core technology exploiting personal big data) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea)”

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Authors and Affiliations

  1. Department of Computer Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do, Republic of Korea

    Dong-Luong Dinh & Sungyoung Lee

  2. Department of Biomedical Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do, Republic of Korea

    Tae-Seong Kim

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  1. Dong-Luong Dinh
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  2. Sungyoung Lee
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  3. Tae-Seong Kim
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Correspondence to Sungyoung Lee or Tae-Seong Kim.

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Dinh, DL., Lee, S. & Kim, TS. Hand number gesture recognition using recognized hand parts in depth images. Multimed Tools Appl 75, 1333–1348 (2016). https://doi.org/10.1007/s11042-014-2370-y

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  • DOI: https://doi.org/10.1007/s11042-014-2370-y

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