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A smart application to detect pupil for small dataset with low illumination

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Abstract

Biometric applications are very sensitive to the process because of its complexity in presenting unstructured input to the processing. The existing applications of image processing are based on the implementation of different programing segments such as image acquisition, segmentation, extraction, and final output. The proposed model is designed with 2 convolution layers and 3 dense layers. We examined the module with 5 datasets including 3 benchmark datasets, namely CASIA, UBIRIS, MMU, random dataset, and the live video. We calculated the FPR, FNR, Precision, Recall, and accuracy of each dataset. The calculated accuracy of CASIA using the proposed system is 82.8%, for UBIRIS is 86%, MMU is 84%, and the random dataset is 84%. On live video with low resolution, calculated accuracy is 72.4%. The proposed system achieved better accuracy compared to existing state-of-the-art systems.

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Authors and Affiliations

  1. Lovely Professional University, Phagwara, Punjab, India

    A. Swathi &  Aarti

  2. Sreyas Institute of Engineering and Technology, Hyderabad, India

    Sandeep Kumar

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  1. A. Swathi
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  2. Aarti
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  3. Sandeep Kumar
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Correspondence to A. Swathi.

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Swathi, A., Aarti & Kumar, S. A smart application to detect pupil for small dataset with low illumination. Innovations Syst Softw Eng 17, 29–43 (2021). https://doi.org/10.1007/s11334-020-00382-3

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  • DOI: https://doi.org/10.1007/s11334-020-00382-3

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