Skip to main content
SpringerLink
Log in

Face recognition performance comparison between fake faces and live faces

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Face recognition is a widely used biometric technology because it is both user friendly and more convenient to use than other biometric approaches. However, naïve face recognition systems that do not support any type of liveness detection can be easily spoofed using just a photograph of a valid user. Face liveness detection is a key issue in the field of security systems that use a camera. Unfortunately, it is not easy to detect face liveness using existing methods, assuming that there are print failures and overall image blur. With the development of display devices and image capturing technology, it is possible to reproduce face images similar to real faces. Therefore, the number of attacks using a photograph or video displayed on a screen rather than paper will increase. In this study, we compare test results using live faces and high-definition face videos from light-emitting diode (LED) display devices and analyze the changes in face recognition performance according to the lighting direction. Experimental results show that there is no significant difference between live faces and not live faces under good lighting conditions. We suggest the use of gamma to reduce the performance gap between the two faces under poor lighting conditions. From these results, we can provide key solutions to resolve the issues associated with texture-based approaches.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Bao W, Li H, Li N, Jiang W (2009) A liveness detection method for face recognition based on optical flow field. In: Proceedings of the 2009 International Conference on Image Analysis and Signal Processing, Taizhou, China, pp 233–236

  • “CASIA Face Anti-Spoofing Database” (2012). http://www.cbsr.ia.ac.cn/english/FaceAntiSpoofDatabases.asp

  • Chakraborty S, Das D (2014) An overview of face liveness detection. Int J Inf Theory 3(2):11–25

    Article  Google Scholar 

  • Chingovska I, Anjos A, Marcel S (2012) On the effectiveness of local binary patterns in face anti-spoofing, Biometrics Special Interest Group (BIOSIG), 2012 BIOSIG-Proceedings of the International Conference of the, IEEE

  • Cho M, Jeong Y (2014) Face recognition performance comparison of fake faces with real faces in relation to lighting. J Internet Services Inf Secur (JISIS) 4(4):82–90

    Google Scholar 

  • Datacolor, “Spyder4ELITE”. http://spyder.datacolor.com

  • Duc NM, Minh BQ (2009) Your face is not your password face authentication bypassing lenovoasustoshiba, Black Hat Briefings

  • Idiap Research Institute, “The Replay-Attack Database” (2012). https://www.idiap.ch/dataset/replayattack

  • IEC 61966-2-1:1999 (1999) Multimedia systems and equipment Colour measurement and management Part 2-1: colour management

  • ISO 15076-1:2010 (2010) Image technology colour management—architecture, profile format and data structure—Part 1: Based on ICC.1:2010

  • Jee HK, Jung SU, Yoo JH (2006) Liveness detection for embedded face recognition system. Int J Biol Med Sci 1(4):235–238

    Google Scholar 

  • Kim G, Eum S, Suhr JK, Kim DI, Park KR, Kim J (2012) Face liveness detection based on texture and frequency analyses, 5th IAPR International Conference on Biometrics (ICB), New Delhi, India, pp 67–72

  • Kim S, Yu S, Kim K, Ban Y, Lee S (2013) Face liveness detection using variable focusing, Biometrics (ICB), 2013 International Conference on, pp 1–6

  • Kollreider K, Fronthaler H, Faraj MI, Bigun J (2007) Real-time face detection and motion analysis with application in liveness assessment. IEEE Trans Inf Forensics Secur 2(3):548–558

    Article  Google Scholar 

  • Kollreider K, Fronthaler H, Bigun J (2005) Evaluating liveness by face images and the structure tensor. In: Proceedings of 4th IEEE Workshop on Automatic Identification Advanced Technologies, Washington DC, USA, pp 75–80

  • Lagorio A, Tistarelli M, Cadoni M (2013) Liveness detection based on 3D face shape analysis, Biometrics and Forensics (IWBF), 2013 International Workshop on, pp 1–4

  • Li SZ, Jain AK (2011) Handbook of face recognition, Chapter 1. Springer, New York

  • Mtt J, Hadid A, Pietikainen M (2011) Face spoofing detection from single images using micro-texture analysis, Biometrics (IJCB), 2011 international joint conference on, IEEE

  • Neurotechnology, “VeriLook”. http://www.neurotechnology.com/verilook.html

  • “NUAA Photograph Imposter Database”(2010). http://parnec.nuaa.edu.cn/xtan/data/nuaaimposterdb.html

  • Pan G, Wu Z, Sun L (2008) Liveness detection for face recognition, recent advances in face recognition. INTECH Open Access Publisher, pp 109–124

  • Penev P, Atick J (1996) Local feature analysis: a general statistical theory for object representation, Netw Comput Neural Syst: 477–500

  • Rowley H, Baluja S, Kanade T (1998) Neural network-based face detection. Pattern Anal Mach Intell IEEE Trans 20(1):23–38

    Article  Google Scholar 

  • Sato A, Imaoka H, Suzuki T, Hosoi T (2005) Advances in face and recognition technologies. NEC J Adv Technol 2(1):28–34

    Google Scholar 

  • Sung KK, Poggio T (1998) Example-based learning for view-based human face detection. Pattern Anal Mach Intell IEEE Trans 20(1):39–51

    Article  Google Scholar 

  • Sun L, Pan G, Wu Z, Lao S (2007) Blinking-based live face detection using conditional random fields, ICB 2007. International Conference, Seoul, Korea, 27–29 Aug 2007, pp 252–260

  • Tan X, Li Y, Liu J, Jiang L (2010) Face liveness detection from a single image with sparse low rank bilinear discriminative model, Computer Vision ECCV 2010. Springer, Berlin Heidelberg

    Google Scholar 

  • Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  • Wiskott L, Fellous JM et al (1997) Face recognition by elastic bunch graph matching. Pattern Anal Mach Intell IEEE Trans 19(7):775–779

    Article  Google Scholar 

  • Yang J, Lei Z, Liao S, Li SZ (2013) Face liveness detection with component dependent descriptor, Biometrics (ICB), 2013 International Conference on, pp 1–6

Download references

Acknowledgments

This work is supported partly by the R&D program of the Korea Ministry of Trade, Industry and Energy(MOTIE) and the Korea Evaluation Institute of Industrial Technology (KEIT). (Project: Technology Development of service robots performance and standardization for movement/manipulation/HRI/Networking, 10041834).

Author information

Authors and Affiliations

  1. Electronics and Telecommunications Research Institute, 218 Gajeongno, Yuseong-gu, Daejeon, 34129, Korea

    Miyoung Cho & Youngsook Jeong

Authors
  1. Miyoung Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Youngsook Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miyoung Cho.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cho, M., Jeong, Y. Face recognition performance comparison between fake faces and live faces. Soft Comput 21, 3429–3437 (2017). https://doi.org/10.1007/s00500-015-2019-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-015-2019-4

Keywords

Search

Navigation