# Robust Face Recognition via Multimodal Deep Face Representation

arXiv: https://arxiv.org/abs/1509.00244 · HTML: https://mlsanity.com/paper/1509.00244/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2015.

## Built on by (most cited first, 19 of 19)

- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors](https://mlsanity.com/paper/2109.02993/) (2021)
- [Adversarial Relighting Against Face Recognition](https://mlsanity.com/paper/2108.07920/) (2021)
- [GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation](https://mlsanity.com/paper/2111.11293/) (2021)
- [Ig3D: Integrating 3D Face Representations in Facial Expression Inference](https://mlsanity.com/paper/2408.16907/) (2024)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization](https://mlsanity.com/paper/2309.09480/) (2023)
- [The Labeled Multiple Canonical Correlation Analysis for Information Fusion](https://mlsanity.com/paper/2103.00359/) (2021)
- [Towards Automated Knowledge Integration From Human-Interpretable Representations](https://mlsanity.com/paper/2402.16105/) (2024)
- [SubFace: Learning with Softmax Approximation for Face Recognition](https://mlsanity.com/paper/2208.11483/) (2022)
- [Ensemble Modeling for Multimodal Visual Action Recognition](https://mlsanity.com/paper/2308.05430/) (2023)
- [ErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models](https://mlsanity.com/paper/2412.17038/) (2024)
- [Cascaded Asymmetric Local Pattern: A Novel Descriptor for Unconstrained Facial Image Recognition and Retrieval](https://mlsanity.com/paper/2201.00518/) (2022)
- [A Survey on Face Recognition Systems](https://mlsanity.com/paper/2201.02991/) (2022)
- [Learning to Minimize the Remainder in Supervised Learning](https://mlsanity.com/paper/2201.09193/) (2022)
- [Convergence of a New Learning Algorithm](https://mlsanity.com/paper/2202.12829/) (2022)
- [A Survey of Deep Fake Detection for Trial Courts](https://mlsanity.com/paper/2205.15792/) (2022)
- [MAVD: The First Open Large-Scale Mandarin Audio-Visual Dataset with Depth Information](https://mlsanity.com/paper/2306.02263/) (2023)
- [PerFace: Metric Learning in Perceptual Facial Similarity for Enhanced Face Anonymization](https://mlsanity.com/paper/2509.20281/) (2025)

## Abstract (arXiv)

Face images appeared in multimedia applications, e.g., social networks and digital entertainment, usually exhibit dramatic pose, illumination, and expression variations, resulting in considerable performance degradation for traditional face recognition algorithms. This paper proposes a comprehensive deep learning framework to jointly learn face representation using multimodal information. The proposed deep learning structure is composed of a set of elaborately designed convolutional neural networks (CNNs) and a three-layer stacked auto-encoder (SAE). The set of CNNs extracts complementary facial features from multimodal data. Then, the extracted features are concatenated to form a high-dimensional feature vector, whose dimension is compressed by SAE. All the CNNs are trained using a subset of 9,000 subjects from the publicly available CASIA-WebFace database, which ensures the reproducibility of this work. Using the proposed single CNN architecture and limited training data, 98.43% verification rate is achieved on the LFW database. Benefited from the complementary information contained in multimodal data, our small ensemble system achieves higher than 99.0% recognition rate on LFW using publicly available training set.