Iris Liveness Detection by Modeling Dynamic Pupil Features

  • Adam CzajkaEmail author
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


The objective of this chapter is to present how to employ pupil dynamics in eye liveness detection. A thorough review of current liveness detection methods is provided at the beginning of the chapter to make the scientific background and position this method within current state-of-the-art methodology. Pupil dynamics may serve as a component of a wider presentation attack detection in iris recognition systems, making them more secure. Due to a lack of public databases that would support this research, we have built our own iris capture device to register pupil size changes under visible light stimuli, and registered 204 observations for 26 subjects (52 different irides), each containing 750 iris images taken every 40 ms. Each measurement registers the spontaneous pupil oscillations and its reaction after a sudden increase and a sudden decrease of the intensity of visible light. The Kohn and Clynes pupil dynamics model is used to describe these changes; hence, we convert each observation into a point in a feature space defined by model parameters. To answer the question whether the eye is alive (that is, if it reacts to light changes as a human eye) or the presentation is suspicious (that is, if it reacts oddly or no reaction is observed), we use linear and nonlinear support vector machines to classify natural reaction and spontaneous oscillations, simultaneously investigating the goodness of fit to reject bad modeling. Our experiments show that this approach can achieve a perfect performance for the data we have collected; all normal reactions are correctly differentiated from spontaneous oscillations. We investigated three variants of modeling to find the simplest, yet still powerful configuration of the method, namely (1) observing the pupil reaction to both the positive and negative changes in the light intensity, (2) using only the pupil reaction to positive surge of the light intensity, and (3) employing only the pupil reaction when the light is suddenly turned off. Further investigation related to the shortest observation time required to model the pupil reaction led to the final conclusion that time periods not exceeding 3 s are adequate to offer a perfect performance (on this dataset).


Local Binary Pattern Iris Image Pupil Size Equal Error Rate Pattern Contact Lens 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author would like to thank Mr. Rafal Brize, who collected the database of iris images used in this work under his Master’s degree project lead by this author. The author is cordially grateful to Prof. Andrzej Pacut of Warsaw University of Technology for valuable remarks that significantly contributed to this research. The application of Kohn and Clynes model was inspired by research of Mr. Marcin Chochowski, who used parameters of this model as individual features in biometric recognition. This author, together with Prof. Pacut and Mr. Chochowski, has been granted a US patent No. 8,061,842 which partially covers the idea deployed in this work.


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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.Warsaw University of TechnologyWarsawPoland
  2. 2.Research and Academic Computer Network (NASK)WarsawPoland

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