# Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification

arXiv: https://arxiv.org/abs/1807.01332 · HTML: https://mlsanity.com/paper/1807.01332/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [Quality-Aware Multimodal Biometric Recognition](https://mlsanity.com/paper/2112.05827/) (2021)
- [Modern Machine and Deep Learning Systems as a way to achieve Man-Computer Symbiosis](https://mlsanity.com/paper/2101.10534/) (2021)
- [Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation](https://mlsanity.com/paper/2109.02804/) (2021)
- [Machine learning approaches for interpretable antibody property prediction using structural data](https://mlsanity.com/paper/2510.23975/) (2025)

## Abstract (arXiv)

In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face, iris, and fingerprint) for person identification. The proposed deep multimodal fusion algorithm consists of multiple streams of modality-specific Convolutional Neural Networks (CNNs), which are jointly optimized at multiple feature abstraction levels. Multiple features are extracted at several different convolutional layers from each modality-specific CNN for joint feature fusion, optimization, and classification. Features extracted at different convolutional layers of a modality-specific CNN represent the input at several different levels of abstract representations. We demonstrate that an efficient multimodal classification can be accomplished with a significant reduction in the number of network parameters by exploiting these multi-level abstract representations extracted from all the modality-specific CNNs. We demonstrate an increase in multimodal person identification performance by utilizing the proposed multi-level feature abstract representations in our multimodal fusion, rather than using only the features from the last layer of each modality-specific CNNs. We show that our deep multi-modal CNNs with multimodal fusion at several different feature level abstraction can significantly outperform the unimodal representation accuracy. We also demonstrate that the joint optimization of all the modality-specific CNNs excels the score and decision level fusions of independently optimized CNNs.