Learning Domain Specific Features using Convolutional Autoencoder: A Vein Authentication Case Study using Siamese Triplet Loss Network

Manish Agnihotri; Aditya Rathod; Daksh Thapar; Gaurav Jaswal; Kamlesh Tiwari; Aditya Nigam

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Paper

Learning Domain Specific Features using Convolutional Autoencoder: A Vein Authentication Case Study using Siamese Triplet Loss Network

Topics: Biometrics; Deep Learning and Neural Networks; Machine Learning Methods

In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 778-785, 2019 , Prague, Czech Republic

Authors: Manish Agnihotri ¹ ; Aditya Rathod ¹ ; Daksh Thapar ² ; Gaurav Jaswal ² ; Kamlesh Tiwari ³ and Aditya Nigam ²

Affiliations: ¹ Department of Information & Communication Technology, Manipal Institute of Technology Manipal, India ; ² School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, India ; ³ Department of Computer Science and Information Systems, Birla Institute of Technology and Science Pilani, India

Keyword(s): Siamese Network, Vascular Biometrics.

Abstract: Recently, deep hierarchically learned models (such as CNN) have achieved superior performance in various computer vision tasks but limited attention has been paid to biometrics till now. This is major because of the number of samples available in biometrics are limited and are not enough to train CNN efficiently. However, deep learning often requires a lot of training data because of the huge number of parameters to be tuned by the learning algorithm. How about designing an end-to-end deep learning network to match the biometric features when the number of training samples is limited? To address this problem, we propose a new way to design an end-to-end deep neural network that works in two major steps: first an auto-encoder has been trained for learning domain specific features followed by a Siamese network trained via. triplet loss function for matching. A publicly available vein image data set has been utilized as a case study to justify our proposal. We observed that transformati ons learned from such a network provide domain specific and most discriminative vascular features. Subsequently, the corresponding traits are matched using multimodal pipelined end-to-end network in which the convolutional layers are pre-trained in an unsupervised fashion as an autoencoder. Thorough experimental studies suggest that the proposed framework consistently outperforms several state-of-the-art vein recognition approaches. (More)

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Paper citation in several formats:

Agnihotri, M.; Rathod, A.; Thapar, D.; Jaswal, G.; Tiwari, K. and Nigam, A. (2019). Learning Domain Specific Features using Convolutional Autoencoder: A Vein Authentication Case Study using Siamese Triplet Loss Network. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-351-3; ISSN 2184-4313, SciTePress, pages 778-785. DOI: 10.5220/0007568007780785

@conference{icpram19,
author={Manish Agnihotri. and Aditya Rathod. and Daksh Thapar. and Gaurav Jaswal. and Kamlesh Tiwari. and Aditya Nigam.},
title={Learning Domain Specific Features using Convolutional Autoencoder: A Vein Authentication Case Study using Siamese Triplet Loss Network},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2019},
pages={778-785},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007568007780785},
isbn={978-989-758-351-3},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Learning Domain Specific Features using Convolutional Autoencoder: A Vein Authentication Case Study using Siamese Triplet Loss Network
SN - 978-989-758-351-3
IS - 2184-4313
AU - Agnihotri, M.
AU - Rathod, A.
AU - Thapar, D.
AU - Jaswal, G.
AU - Tiwari, K.
AU - Nigam, A.
PY - 2019
SP - 778
EP - 785
DO - 10.5220/0007568007780785
PB - SciTePress