Abstract
Shoeprint is an important evidence for crime investigation. Many automatic shoeprint retrieval methods have been proposed in order to efficiently provide useful information for the identification of the criminals. In the mean time, the convolutional neural network shows great capacity in image classification problem but its application in shoeprint retrieval is not yet investigated. This paper presents an application of VGG16 network as feature extractor in shoeprint retrieval and a data augmentation method to fine-tune the neural network with a very small database. Our method shows a much better performance compared with state-of-the-art methods on a same database with crime-scene-like shoeprints.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alexandre, G.: Computerized classification of the shoeprints of burglars’ soles. Forensic Sci. Int. 82(1), 59–65 (1996)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_53
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Geradts, Z., Keijzer, J.: The image-database REBEZO for shoeprints with developments on automatic classification of shoe outsole designs. Forensic Sci. Int. 82(1), 21–31 (1996)
Alexander, A., Bouridane, A., Crookes, D.: Automatic classification and recognition of shoeprints (1999)
AlGarni, G., Hamiane, M.: A novel technique for automatic shoeprint image retrieval. Forensic Sci. Int. 181(1), 10–14 (2008)
Xiao, R., Shi, P.: Computerized matching of shoeprints based on sole pattern. Comput. Forensics 5158, 96–104 (2008)
De Chazal, P., Flynn, J., Reilly, R.B.: Automated processing of shoeprint images based on the fourier transform for use in forensic science. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 341–350 (2005)
Gueham, M., Bouridane, A., Crookes, D.: Automatic recognition of partial shoeprints based on phase-only correlation. In: IEEE International Conference on Image Processing. vol. 4, pp. IV–441. IEEE (2007)
Cervelli, F., Dardi, F., Carrato, S.: A translational and rotational invariant descriptor for automatic footwear retrieval of real cases shoe marks. In: 2010 18th European Signal Processing Conference, pp. 1665–1669. IEEE (2010)
Gueham, M., Bouridane, A., Crookes, D., Nibouche, O.: Automatic recognition of shoeprints using Fourier-Mellin transform. In: NASA/ESA Conference on Adaptive Hardware and Systems, pp. 487–491. IEEE (2008)
Patil, P.M., Kulkarni, J.V.: Rotation and intensity invariant shoeprint matching using Gabor transform with application to forensic science. Pattern Recogn. 42(7), 1308–1317 (2009)
Li, Z., Wei, C., Li, Y., Sun, T.: Research of shoeprint image stream retrival algorithm with scale-invariance feature transform. In: International Conference on Multimedia Technology (ICMT), pp. 5488–5491. IEEE (2011)
Srihari, S.N.: Analysis of Footwear Impression Evidence. BiblioGov, Columbus (2012)
Cervelli, F., Dardi, F., Carrato, S.: Comparison of footwear retrieval systems for synthetic and real shoe marks. In: Image and Signal Processing and Analysis, pp. 684–689. IEEE (2009)
Richetelli, N., Lee, M.C., Lasky, C.A., Gump, M.E., Speir, J.A.: Classification of footwear outsole patterns using fourier transform and local interest points. Forensic Sci. Int. 275, 102–109 (2017)
Athiwaratkun, B., Kang, K.: Feature representation in convolutional neural networks. arXiv preprint arXiv:1507.02313 (2015)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: arXiv preprint arXiv:1409.1556 (2014)
Chollet, F., et al.: Keras. https://github.com/fchollet/keras (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhang, Y., Fu, H., Dellandréa, E., Chen, L. (2017). Adapting Convolutional Neural Networks on the Shoeprint Retrieval for Forensic Use. 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_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-69923-3_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69922-6
Online ISBN: 978-3-319-69923-3
eBook Packages: Computer ScienceComputer Science (R0)