Liveness Detection Based on Eye Flicker

  • Rekha A. Shidnekoppa
  • Manjunath Kammar
  • K. S. Shreedhar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 801)


Many of the methods has been reported for detection of liveness based on eye flicker are intrusive, in our method detecting liveness using non intrusive. The proposed methodology uses eye flicker activity in an image sequences. The present generic camera will capture the face video with twenty five FPS, frame intervals difference is less than seventy milliseconds. Sequences of images are used in AdaBoost method and Viola Jones technique for the face identification. The Harr like components are figured by convolving the picture with layouts of various size and introduction. Next step is to detect eye region, the position of eye in a face is found on certain geometric dependencies known for human face. If iris is detected in eye region then eye is open. If not, it is closed. Here in this paper we are thinking about eye flicker and iris varieties as a confirmation of liveness to reject utilizing few fake. We tested around more than 20 natural videos to detect the liveness based on eye flicker and achieved 92.50% accuracy.


Face detection Iris recognition Eye flicker Haar features Viola Jones Liveness detection 


  1. 1.
    Annu, Kant, C.: Liveness detection in face recognition using euclidean distances. Int. J. Adv. Res. Eng. Technol. 1, 1–5 (2013)Google Scholar
  2. 2.
    Kant, C., Sharma, N.: Fake face detection based on skin elasticity. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3, 1048–1051 (2013)Google Scholar
  3. 3.
    Czajka, A.: Database of iris printouts and its application: development of liveness detection method for iris recognition. Institute of Control and Computation Engineering Warsaw University of Technology, ul. NowowiejskaWarsaw, Poland, pp. 26–29 (2013)Google Scholar
  4. 4.
    Kim, G., Eum, S., Suhr, J.K., Kim, D.I., Park, K.R., Kim, J.: Face liveness detection based on texture and frequency analyses. Research Institute of Automotive Electronics and Control, Hanyang University, Republic of Korea, pp. 1–5 (2012)Google Scholar
  5. 5.
    Bhatt, B.G., Shah, Z.H.: Face feature extraction techniques: a survey. In: National Conference on Recent Trends in Engineering & Technology, pp. 13–14 (2011)Google Scholar
  6. 6.
    Zhang, H., Sun, Z., Tan, T., Wang, J.: Learning hierarchical visual codebook for iris liveness detection. Shanghai Institute of Technical Physics, Chinese Academy of Sciences, pp. 1–5 (2011)Google Scholar
  7. 7.
    Nadimi, S., Bhanu, B.: Physical models for moving shadow and object detection in video. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1079–1087 (2004)CrossRefGoogle Scholar
  8. 8.
    Ni, B., Kassim, A.A., Winkler, S.: A hybrid framework for 3D human motion tracking. IEEE Trans. Circ. Syst. Video Technol. 18, 1075–1084 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Tontadarya College of EngineeringGadagIndia
  2. 2.University BDT College of EngineeringDavanagereIndia

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