The eye region is one of the most attractive sources for identification and verification due to the representative availability of such biometric modalities as periocular and iris. Many score-level fusion approaches have been proposed to combine these two modalities targeting to improve the robustness. The score-level approaches can be grouped into three categories: transformation-based, classification-based and density-based. Each category has its own benefits, if combined can lead to a robust fusion mechanism. In this paper, we propose a hierarchical fusion network to fuse multiple fusion approaches from transformation-based and classification-based categories into a unified framework for classification. The proposed hierarchical approach relies on the universal approximation theorem for neural networks to approximate each fusion approach as one child neural network and then ensemble them into a unified parent network. This mechanism takes advantage of both categories to improve the fusion performance, illustrated by an improved equal error rate of the multimodal biometric system. We subsequently force the parent network to learn the representation and interaction strategy between the child networks from the training data through a sparse autoencoder layer, leading to further improvements. Experiments on two public datasets (MBGC version 2 and CASIA-Iris-Thousand) and our own dataset validate the effectiveness of the proposed hierarchical fusion approach for periocular and iris modalities.

由于诸如眼周和虹膜等生物特征识别方法的代表性可用性，眼部区域是用于识别和验证的最有吸引力的来源之一。已经提出了许多分数级融合方法来结合这两种方式，以提高鲁棒性。得分级别的方法可以分为三类：基于转换，基于分类和基于密度。每个类别都有其自身的优势，如果将它们组合在一起，可以带来强大的融合机制。在本文中，我们提出了一种分层融合网络将多种融合方法从基于转换的类别和基于分类的类别融合到一个统一的分类框架中。所提出的分层方法依赖于神经网络的通用逼近定理，将每种融合方法近似为一个子神经网络，然后将它们融合为一个统一的父网络。这种机制利用了这两种类别的优势来改善融合性能，这表现为多模式生物特征识别系统的均等错误率提高。随后，我们迫使父级网络通过稀疏的自动编码器层从训练数据中学习子级网络之间的表示和交互策略，从而实现进一步的改进。