Skip to main content
SpringerLink
Log in

Deep feature representation and ball-tree for face sketch recognition

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Forensic face sketch-photo recognition attracts considerable interest in the law enforcement agencies. This paper proposes a new face sketch-photo recognition method based on the VGG deep feature and ball-tree searching algorithm. In this paper, the recognition performances by different feature layers of pretrained VGG-Face model are explored. In addition, to accelerate the matching speed, the ball-tree algorithm is adopted to search the nearest neighbors of query sketches from gallery photos. The experimental results on CUFS and IIIT-D datasets demonstrate the superiority of the proposed method compared with existing algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Alex A, Asari V, Mathew A (2013) Local difference of gaussian binary pattern: Robust features for face sketch recognition. In: Proceedings of the IEEE international conference on systems, man and cybernetics, pp 1211–1216

  • Bhatt HS, Bharadwaj S, Singh R, Vatsa M (2011) Memetic approach for matching sketches with digital face images. IIITD-TR-2011-006

  • Galea C, Farrugia RA (2016) Face photo-sketch recognition using local and global texture descriptors. In: 2016 24th European signal processing conference (EUSIPCO). IEEE, pp 2240–2244

  • Galea C, Farrugia RA (2017) Forensic face photo-sketch recognition using a deep learning-based architecture. IEEE Signal Process Lett 24(11):1586–1590

    Article  Google Scholar 

  • Galoogahi H, Sim T (2012) Inter-modality face sketch recognition. In: ICME, pp 224–229

  • Gao X, Zhong J, Li J, Tian C (2008) Face sketch synthesis using E-HMM and selective ensemble. IEEE Trans Circuits Syst Video Technol 18(4):487–496

    Article  Google Scholar 

  • Kan M, Shan S, Zhang H, Lao S, Chen X (2012) Multi-view discriminant analysis. In: Proceedings of 12th European conference computer vision, pp 808–821

  • Kazemi H, Soleymani S, Dabouei A, Iranmanesh M, Nasrabadi NM (2018) Attribute-centered loss for soft-biometrics guided face sketch-photo recognition. arXiv preprint arXiv:1804.03082

  • King DE (2009) Dlib-ml: a machine learning toolkit. J Mach Learn Res 10:1755–1758

    Google Scholar 

  • Klare B, Jain AK (2010) Sketch-to-photo matching: a feature-based approach. In: Proceedings of society of photo-optical instrumentation engineers conference series, p 7667

  • Klare B, Li Z, Jain A (2011) Matching forensic sketches to mugshot photos. IEEE Trans Pattern Anal Mach Intell 33(3):639–646

    Article  Google Scholar 

  • Liu T, Moore AW, Gray A (2006) New algorithms for efficient high-dimensional nonparametric classification. J Mach Learn Res 7:1135–1158

    MathSciNet  MATH  Google Scholar 

  • Mignon A, Jurie F (2012) CMML: a new metric learning approach for cross modal matching. In: Proceedings of Asian conference computer vision, pp 1–14

  • Mittal P, Vatsa M, Singh R (2015) Composite sketch recognition via deep network-a transfer learning approach. In: 2015 international conference on biometrics (ICB). IEEE, pp 251–256

  • Parkhi OM, Andrea V, Andrew Z (2015) Deep face recognition. In: British machine vision conference, vol 1, no 3

  • Peng C, Gao X, Wang N, Li J (2017) Graphical representation for heterogeneous face recognition. IEEE Trans Pattern Anal Mach Intell 39(2):301–312

    Article  Google Scholar 

  • Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823

  • Vedaldi A, Lenc K (2015) MatConvNet: convolutional neural networks for MATLAB. In: Proceedings of the 23rd ACM international conference on multimedia, pp 689–692

  • Zhang W, Wang X, Tang X (2011) Coupled information-theoretic encoding for face photo-sketch recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 513–520

  • Zhang M, Li J, Wang N, Gao X (2015) Recognition of facial sketch styles. Neurocomputing 149:1188–1197

    Article  Google Scholar 

  • Zhou H, Kuang Z, Wong K (2012) Markov weight fields for face sketch synthesis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1091–1097

Download references

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (GR 2016R1D1A3B03931911).

Author information

Authors and Affiliations

  1. Division of Computer Science and Engineering, Chonbuk National University, Jeonju, Korea

    Weiguo Wan & Hyo Jong Lee

  2. Center for Advanced Image and Information Technology, Chonbuk National University, Jeonju, Korea

    Hyo Jong Lee

Authors
  1. Weiguo Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyo Jong Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hyo Jong Lee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wan, W., Lee, H.J. Deep feature representation and ball-tree for face sketch recognition. Int J Syst Assur Eng Manag 11, 818–823 (2020). https://doi.org/10.1007/s13198-019-00882-x

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-019-00882-x

Keywords

Search

Navigation