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Identifying twins based on ocular region features using deep representations

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Abstract

In this paper, we tackle the twins identification problem; a challenging task in biometric authentication. The experiments are carried out using deep learning techniques and for that we have proposed a Convolutional Siamese Network (CNN-Siamese) and a multiscale Convolutional Siamese network (MCNN-Siamese). We have also explored and evaluated three pre-trained CNNs namely, ResNet-50, VGG-16 and NASNet-Large, and utilized them in Siamese network after appropriate modifications. In addition, a simple 5-layer neural network (sNN) is also utilised as Siamese subnetwork in the experiments. We have presented compelling experimental evidences in terms of correct classification accuracy (CCR) on CASIA-IrisV4 Twins’ dataset that manifest the effectiveness of ocular biometrics in identifying twins. The results achieve the existing state-of-the-art human level accuracy. Also, the proximity of CCRs of all the models asserts that the ocular regions in twins hold a significant correlation to label them as twins. We have also quantified the cross-domain capability of the proposed subnetworks (i.e. CNN and MCNN) on ND-GFI dataset that outperforms the state-of-the-art methods. Notably, for the ocular biometrics, to the best of our knowledge, there is currently no literature available as of date that explores the association between twins and subsequently unriddles the classification using Deep Learning.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, India

    Gunjan Gautam & Susanta Mukhopadhyay

  2. Department of Electrical Engineering, Indian Institute of Technology (ISM), Dhanbad, India

    Aditya Raj

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  1. Gunjan Gautam
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  2. Aditya Raj
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  3. Susanta Mukhopadhyay
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Correspondence to Gunjan Gautam.

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Gautam, G., Raj, A. & Mukhopadhyay, S. Identifying twins based on ocular region features using deep representations. Appl Intell 51, 1–18 (2021). https://doi.org/10.1007/s10489-019-01562-w

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