Journal of Multimedia Information System

Korea Multimedia Society (한국멀티미디어학회)
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Mobile Palmprint Segmentation Based on Improved Active Shape Model

  • Gao, Fumeng (Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, School of Software, Nanchang Hangkong University) ;
  • Cao, Kuishun (Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, School of Software, Nanchang Hangkong University) ;
  • Leng, Lu (Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, School of Software, Nanchang Hangkong University) ;
  • Yuan, Yue (Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, School of Software, Nanchang Hangkong University)
  • Received : 2018.12.24
  • Accepted : 2018.12.27
  • Published : 2018.12.31
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Abstract

Skin-color information is not sufficient for palmprint segmentation in complex scenes, including mobile environments. Traditional active shape model (ASM) combines gray information and shape information, but its performance is not good in complex scenes. An improved ASM method is developed for palmprint segmentation, in which Perux method normalizes the shape of the palm. Then the shape model of the palm is calculated with principal component analysis. Finally, the color likelihood degree is used to replace the gray information for target fitting. The improved ASM method reduces the complexity, while improves the accuracy and robustness.

Keywords

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Fig. 1. Dual-restriction-box assistance.

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Fig. 2. Samples for contour marking.

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Fig. 3. Boundary point set. (a) Sub-image in restriction box, (b) Manual marking (11 points), (c) Piece-wise linear interpolation (31 points).

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Fig. 4. Key point detection. (a) Feature points, (b) Two fitted lines, (c) Angle bisector, (d) Key point.

Table 1. Accuracy comparison.

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