Chih-Ying Gwo1, Chia-Hung Wei2. 1. Department of Information Management, Chien Hsin University of Science and Technology, Taiwan. Electronic address: ericgwo@uch.edu.tw. 2. Department of Information Management, Chien Hsin University of Science and Technology, Taiwan; Graduate Institute of Biomedical Informatics, Taipei Medical University, Taiwan. Electronic address: rogerwei@uch.edu.tw.
Abstract
PURPOSE: Shoeprint recognition has been widely used as forensic evidence in criminal cases. The purpose of this study is to propose a shoeprint retrieval method based on core point alignment for pattern analysis. METHOD: The proposed method firstly detects contour points in a black-and-white shoeprint image. These reliable contour points are selected to simulate the left and right sidelines of the shoeprint by a curve fitting method. Subsequently, the most concave points along the left and right sidelines can determine the core point of the shoeprint, thereby partitioning the shoeprint into circular regions. Next, the Zernike moments of the circular regions are calculated for pattern descriptions of each region. Finally, the Euclidean distance is measured to match the shoeprints with the same pattern. RESULT: The highest APR=0.726 is obtained from the first four Zernike moments with a radius of 90pixels and three baselines. The experimental results also show that the Zernike method in any order always outperforms the compared moment invariant and GLCM method. The experimental results also indicate that the core point is more stable than the gravity center in the both sets, because the standard deviation values of the core point are less than that of the gravity center. CONCLUSIONS: This study has verified that the proposed method can effectively align shoeprints for pattern comparison.
PURPOSE: Shoeprint recognition has been widely used as forensic evidence in criminal cases. The purpose of this study is to propose a shoeprint retrieval method based on core point alignment for pattern analysis. METHOD: The proposed method firstly detects contour points in a black-and-white shoeprint image. These reliable contour points are selected to simulate the left and right sidelines of the shoeprint by a curve fitting method. Subsequently, the most concave points along the left and right sidelines can determine the core point of the shoeprint, thereby partitioning the shoeprint into circular regions. Next, the Zernike moments of the circular regions are calculated for pattern descriptions of each region. Finally, the Euclidean distance is measured to match the shoeprints with the same pattern. RESULT: The highest APR=0.726 is obtained from the first four Zernike moments with a radius of 90pixels and three baselines. The experimental results also show that the Zernike method in any order always outperforms the compared moment invariant and GLCM method. The experimental results also indicate that the core point is more stable than the gravity center in the both sets, because the standard deviation values of the core point are less than that of the gravity center. CONCLUSIONS: This study has verified that the proposed method can effectively align shoeprints for pattern comparison.
Authors: Martin Baiker-Sørensen; Koen Herlaar; Isaac Keereweer; Petra Pauw-Vugts; Richard Visser Journal: Forensic Sci Int Date: 2020-04-02 Impact factor: 2.395