Few-Shot Learning for Biometric Verification

TL;DR

This paper introduces a lightweight, hybrid few-shot learning approach for biometric verification that combines shallow neural networks with traditional features, effectively handling data scarcity and reducing false acceptance.

Contribution

A novel end-to-end lightweight architecture that integrates shallow networks with hand-crafted features for biometric verification using few-shot learning techniques.

Findings

Achieved competitive accuracy with state-of-the-art methods.

Effectively handled data scarcity in biometric datasets.

Introduced a self-estimated threshold to monitor FAR without bias.

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.