Few-Shot Learning for Biometric Verification
- URL: http://arxiv.org/abs/2211.06761v2
- Date: Wed, 3 May 2023 15:24:02 GMT
- Title: Few-Shot Learning for Biometric Verification
- Authors: Saad Bin Ahmed, Umaid M. Zaffar, Marium Aslam and Muhammad Imran Malik
- Abstract summary: In machine learning applications, it is common practice to feed as much information as possible. In most cases, the model can handle large data sets that allow to predict more accurately.
We propose a novel end-to-end lightweight architecture that verifies biometric data by producing competitive results as compared to state-of-the-art accuracies through Few-Shot learning methods.
- Score: 2.3226893628361682
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In machine learning applications, it is common practice to feed as much
information as possible. In most cases, the model can handle large data sets
that allow to predict more accurately. In the presence of data scarcity, a
Few-Shot learning (FSL) approach aims to build more accurate algorithms with
limited training data. We propose a novel end-to-end lightweight architecture
that verifies biometric data by producing competitive results as compared to
state-of-the-art accuracies through Few-Shot learning methods. The dense layers
add to the complexity of state-of-the-art deep learning models which inhibits
them to be used in low-power applications. In presented approach, a shallow
network is coupled with a conventional machine learning technique that exploits
hand-crafted features to verify biometric images from multi-modal sources such
as signatures, periocular region, iris, face, fingerprints etc. We introduce a
self-estimated threshold that strictly monitors False Acceptance Rate (FAR)
while generalizing its results hence eliminating user-defined thresholds from
ROC curves that are likely to be biased on local data distribution. This hybrid
model benefits from few-shot learning to make up for scarcity of data in
biometric use-cases. We have conducted extensive experimentation with commonly
used biometric datasets. The obtained results provided an effective solution
for biometric verification systems.
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