A-iLearn: An adaptive incremental learning model for spoof fingerprint detection

Incremental learning enables the learner to accommodate new knowledge without retraining the existing model. It is a challenging task that requires learning from new data and preserving the knowledge extracted from the previously accessed data. This challenge is known as the stability-plasticity dil...

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Autores principales:	Shivang Agarwal, Ajita Rattani, C. Ravindranath Chowdary
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Incremental learning Stability-plasticity dilemma Catastrophic forgetting Spoof fingerprint detection Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/b7ba07c72783430c8d1e5af62c2dcb49
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id	oai:doaj.org-article:b7ba07c72783430c8d1e5af62c2dcb49
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spelling	oai:doaj.org-article:b7ba07c72783430c8d1e5af62c2dcb492021-11-28T04:39:29ZA-iLearn: An adaptive incremental learning model for spoof fingerprint detection2666-827010.1016/j.mlwa.2021.100210https://doaj.org/article/b7ba07c72783430c8d1e5af62c2dcb492022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666827021001055https://doaj.org/toc/2666-8270Incremental learning enables the learner to accommodate new knowledge without retraining the existing model. It is a challenging task that requires learning from new data and preserving the knowledge extracted from the previously accessed data. This challenge is known as the stability-plasticity dilemma. We propose A-iLearn, a generic model for incremental learning which overcomes the stability-plasticity dilemma by carefully integrating the ensemble of base classifiers trained on new data with the current ensemble without retraining the model from scratch using entire data. We demonstrate the efficacy of the proposed A-iLearn model on spoof fingerprint detection application. One of the significant challenges associated with spoof fingerprint detection is the performance drop on spoofs generated using new fabrication materials. A-iLearn is an adaptive incremental learning model that adapts to the features of the “live” and “spoof” fingerprint images and efficiently recognizes the new spoof fingerprints and the known spoof fingerprints when the new data is available. To the best of our knowledge, A-iLearn is the first attempt in incremental learning algorithms that adapts to the properties of data for generating a diverse ensemble of base classifiers. From the experiments conducted on standard high-dimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015, we show that the performance gain on new fake materials is significantly high. On average, we achieve 49.57% improvement in accuracy between the consecutive learning phases.Shivang AgarwalAjita RattaniC. Ravindranath ChowdaryElsevierarticleIncremental learningStability-plasticity dilemmaCatastrophic forgettingSpoof fingerprint detectionCyberneticsQ300-390Electronic computers. Computer scienceQA75.5-76.95ENMachine Learning with Applications, Vol 7, Iss , Pp 100210- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Incremental learning Stability-plasticity dilemma Catastrophic forgetting Spoof fingerprint detection Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Incremental learning Stability-plasticity dilemma Catastrophic forgetting Spoof fingerprint detection Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95 Shivang Agarwal Ajita Rattani C. Ravindranath Chowdary A-iLearn: An adaptive incremental learning model for spoof fingerprint detection
description	Incremental learning enables the learner to accommodate new knowledge without retraining the existing model. It is a challenging task that requires learning from new data and preserving the knowledge extracted from the previously accessed data. This challenge is known as the stability-plasticity dilemma. We propose A-iLearn, a generic model for incremental learning which overcomes the stability-plasticity dilemma by carefully integrating the ensemble of base classifiers trained on new data with the current ensemble without retraining the model from scratch using entire data. We demonstrate the efficacy of the proposed A-iLearn model on spoof fingerprint detection application. One of the significant challenges associated with spoof fingerprint detection is the performance drop on spoofs generated using new fabrication materials. A-iLearn is an adaptive incremental learning model that adapts to the features of the “live” and “spoof” fingerprint images and efficiently recognizes the new spoof fingerprints and the known spoof fingerprints when the new data is available. To the best of our knowledge, A-iLearn is the first attempt in incremental learning algorithms that adapts to the properties of data for generating a diverse ensemble of base classifiers. From the experiments conducted on standard high-dimensional datasets LivDet 2011, LivDet 2013 and LivDet 2015, we show that the performance gain on new fake materials is significantly high. On average, we achieve 49.57% improvement in accuracy between the consecutive learning phases.
format	article
author	Shivang Agarwal Ajita Rattani C. Ravindranath Chowdary
author_facet	Shivang Agarwal Ajita Rattani C. Ravindranath Chowdary
author_sort	Shivang Agarwal
title	A-iLearn: An adaptive incremental learning model for spoof fingerprint detection
title_short	A-iLearn: An adaptive incremental learning model for spoof fingerprint detection
title_full	A-iLearn: An adaptive incremental learning model for spoof fingerprint detection
title_fullStr	A-iLearn: An adaptive incremental learning model for spoof fingerprint detection
title_full_unstemmed	A-iLearn: An adaptive incremental learning model for spoof fingerprint detection
title_sort	a-ilearn: an adaptive incremental learning model for spoof fingerprint detection
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/b7ba07c72783430c8d1e5af62c2dcb49
work_keys_str_mv	AT shivangagarwal ailearnanadaptiveincrementallearningmodelforspooffingerprintdetection AT ajitarattani ailearnanadaptiveincrementallearningmodelforspooffingerprintdetection AT cravindranathchowdary ailearnanadaptiveincrementallearningmodelforspooffingerprintdetection
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A-iLearn: An adaptive incremental learning model for spoof fingerprint detection

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