# Deep learning and face recognition: the state of the art

arXiv: https://arxiv.org/abs/1902.03524 · HTML: https://mlsanity.com/paper/1902.03524/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2019.

## Built on by (most cited first, 11 of 11)

- [Unrestricted Black-box Adversarial Attack Using GAN with Limited Queries](https://mlsanity.com/paper/2208.11613/) (2022)
- [Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization](https://mlsanity.com/paper/2404.08154/) (2024)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [Inclusive Portraits: Race-Aware Human-in-the-Loop Technology](https://mlsanity.com/paper/2311.03567/) (2023)
- [Re:Draw -- Context Aware Translation as a Controllable Method for Artistic Production](https://mlsanity.com/paper/2401.03499/) (2024)
- [Bridging the Gap between Artificial Intelligence and Artificial General Intelligence: A Ten Commandment Framework for Human-Like Intelligence](https://mlsanity.com/paper/2210.09366/) (2022)
- [SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control](https://mlsanity.com/paper/2202.01816/) (2022)
- [A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data](https://mlsanity.com/paper/2311.10025/) (2023)
- [DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localization](https://mlsanity.com/paper/2212.04575/) (2022)
- [Deep Learning-based Spatio Temporal Facial Feature Visual Speech Recognition](https://mlsanity.com/paper/2305.00552/) (2023)
- [The Eminence in Shadow: Exploiting Feature Boundary Ambiguity for Robust Backdoor Attacks](https://mlsanity.com/paper/2512.10402/) (2025)

## Abstract (arXiv)

Deep Neural Networks (DNNs) have established themselves as a dominant technique in machine learning. DNNs have been top performers on a wide variety of tasks including image classification, speech recognition, and face recognition. Convolutional neural networks (CNNs) have been used in nearly all of the top performing methods on the Labeled Faces in the Wild (LFW) dataset. In this talk and accompanying paper, I attempt to provide a review and summary of the deep learning techniques used in the state-of-the-art. In addition, I highlight the need for both larger and more challenging public datasets to benchmark these systems. The high accuracy (99.63% for FaceNet at the time of publishing) and utilization of outside data (hundreds of millions of images in the case of Google's FaceNet) suggest that current face verification benchmarks such as LFW may not be challenging enough, nor provide enough data, for current techniques. There exist a variety of organizations with mobile photo sharing applications that would be capable of releasing a very large scale and highly diverse dataset of facial images captured on mobile devices. Such an "ImageNet for Face Recognition" would likely receive a warm welcome from researchers and practitioners alike.