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An augmented image gradients based supervised regression technique for iris center localization

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Abstract

This paper describes a robust and accurate technique for iris center localization by combining supervised regression based approach and image gradients. The proposed work consist of two stages. The first stage comprises regression approach which is based upon learning of local binary features to detect the periocular regions. In the second stage, image gradients were applied to the extracted eye patch regions to detect the accurate iris centers. The proposed augmented image gradients based supervised regression approach tested on the two publicly available challenging datasets show good accuracy. The results proved that supervised regression technique when augmented with image gradients approach improved the accuracy of iris center detection on the face image acquired under unconstraint conditions. The outcome of the proposed work suggests that by augmenting effective unsupervised techniques such as image gradients improves the accuracy and robustness of the supervised approaches used for face alignment applications. This work may be extended towards the development of accurate and fast eye gaze tracking systems.

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Acknowledgments

The authors are thankful to the image dataset providers for making available the large datasets along with its accurate ground truth information.

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Authors and Affiliations

  1. Biomedical Instrumentation (V-02), CSIR-Central Scientific Instruments Organisation (CSIO)|, Sector 30-C, Chandigarh, India, 160030

    Amit Laddi

  2. Department of ECE, PEC University of Technology, Sector 12, Chandigarh, India, 160012

    Neelam Rup Prakash

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  1. Amit Laddi
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  2. Neelam Rup Prakash
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Correspondence to Amit Laddi.

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Laddi, A., Prakash, N.R. An augmented image gradients based supervised regression technique for iris center localization. Multimed Tools Appl 76, 7129–7139 (2017). https://doi.org/10.1007/s11042-016-3361-y

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  • DOI: https://doi.org/10.1007/s11042-016-3361-y

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