# A Study of Multimodal Person Verification Using Audio-Visual-Thermal Data

arXiv: https://arxiv.org/abs/2110.12136 · HTML: https://mlsanity.com/paper/2110.12136/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 23 Oct 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://mlsanity.com/paper/1905.00641/) (2019)
- [In defence of metric learning for speaker recognition](https://mlsanity.com/paper/2003.11982/) (2020)
- [DeepID3: Face Recognition with Very Deep Neural Networks](https://mlsanity.com/paper/1502.00873/) (2015)
- [BUT System Description to VoxCeleb Speaker Recognition Challenge 2019](https://mlsanity.com/paper/1910.12592/) (2019)
- [Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System](https://mlsanity.com/paper/1804.05160/) (2018)
- [Noise-tolerant Audio-visual Online Person Verification using an Attention-based Neural Network Fusion](https://mlsanity.com/paper/1811.10813/) (2018)
- [SpeakingFaces: A Large-Scale Multimodal Dataset of Voice Commands with Visual and Thermal Video Streams](https://mlsanity.com/paper/2012.02961/) (2020)
- [Biometrics Recognition Using Deep Learning: A Survey](https://mlsanity.com/paper/1912.00271/) (2019)

## Abstract (arXiv)

In this paper, we study an approach to multimodal person verification using audio, visual, and thermal modalities. The combination of audio and visual modalities has already been shown to be effective for robust person verification. From this perspective, we investigate the impact of further increasing the number of modalities by adding thermal images. In particular, we implemented unimodal, bimodal, and trimodal verification systems using state-of-the-art deep learning architectures and compared their performance under clean and noisy conditions. We also compared two popular fusion approaches based on simple score averaging and the soft attention mechanism. The experiment conducted on the SpeakingFaces dataset demonstrates the superior performance of the trimodal verification system. Specifically, on the easy test set, the trimodal system outperforms the best unimodal and bimodal systems by over 50% and 18% relative equal error rates, respectively, under both the clean and noisy conditions. On the hard test set, the trimodal system outperforms the best unimodal and bimodal systems by over 40% and 13% relative equal error rates, respectively, under both the clean and noisy conditions. To enable reproducibility of the experiment and facilitate research into multimodal person verification, we made our code, pretrained models, and preprocessed dataset freely available in our GitHub repository.