Abstract
In Chinese forensic science, a three-dimensional footprint can provide us lots of information, such as sex, age and gait. Dig-imprint is one of the impressions in three-dimensional footprints that can show the biometric. However, the three-dimensional footprints are still analyzed artificially by forensic investigators, which is inefficient and subjective. In this research an algorithm for the automatic detection of dig-imprints of three-dimensional footprints was developed. Haar-like and LBP features were extracted from the dataset. Next, two classifiers were constructed with Adaboost algorithm using these two features. A dig-imprint database is constructed for evaluating the performance of the proposed method. Pictures of three-dimensional footprints were taken by the way of criminal scene photography. Then the dig-imprints were cut out as positive samples. The negative samples were also cut out from three-dimensional footprints. Experimental results shows that the proposed method achieves good detection accuracy.
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References
Shi, L., et al.: Footprint足迹学. People’s Public Security University of China, Beijing (2007)
Queen, R.M., Abbey, A.N., Wiegerinck, J.I., et al.: Effect of shoe type on plantar pressure: a gender comparison. J. Gait Posture 31, 18–22 (2013)
Xu, S., Zhou, X., Sun, Y.: A novel platform system for gait analysis. In: Proceedings of the 2008 International Conference on Human System Interactions, pp. 1045–1049 (2008)
Manfio, E.F., Nadal, J., Muniz, A.M.S., et al.: Principal component analysis of vertical ground reaction force: a powerful method to discriminate normal and abnormal gait and assess treatment. In: International Conference of the IEEE Engineering in Medicine & Biology Society, pp. 2294–2297. IEEE, New York (2006)
Han, D., Yunqi, T., Wei, G.: Research on the stability of plantar pressure under normal walking condition. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds.) CCPR 2016. CCIS, vol. 662, pp. 234–242. Springer, Singapore (2016). doi:10.1007/978-981-10-3002-4_20
Viola, P., Jones, M.J.: Robust Real-Time Face Detection. Kluwer Academic Publishers, Netherlands (2004)
Zhao, Y., Gong, L., Zhou, B., Huang, Y., Liu, C.: Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis. J. Biosyst. Eng. 148, 127–137 (2016)
Tambasco Bruno, D.O., do Nascimento, M.Z., Ramos, R.P., Batista, V.R., Neves, L.A., Martins, A.S.: LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues. J. Expert Syst. Appl. 55, 329–340 (2016)
García-Olalla, O., Alegre, E., Barreiro, J., et al.: Tool wear classification using LBP-based descriptors combined with LOSIB-based enhancers. J. Procedia Eng. 132, 950–957 (2015)
Li, K., Zhang, G., Wang, Y., Wang, P., Ni, C.: Hand-dorsa vein recognition based on improved partition local binary patterns. Biometric Recognition. LNCS, vol. 9428, pp. 312–320. Springer, Cham (2015). doi:10.1007/978-3-319-25417-3_37
Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: Proceedings of International Conference on Computer Vision, pp. 555–562. IEEE Press, New York (1998)
Viola, P., Jones, M.: Rapid object detection using a boost cascade of simple features. In: Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 511–518. IEEE Press, New York (2001)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. J. Pattern Recognit. 29, 51–59 (1996)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with Local Binary Patterns. J. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)
Goldberg, D.E.: Genetic algorithm in search. J. Optim. Mach. Learn. 1(7), 2104–2116 (1989)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 61503387).
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Sun, H., Tang, Y., Guo, W. (2017). Research on Dig-Imprint Detection of Three-Dimensional Footprints. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_53
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DOI: https://doi.org/10.1007/978-3-319-69923-3_53
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