# Targeting Ultimate Accuracy: Face Recognition via Deep Embedding

arXiv: https://arxiv.org/abs/1506.07310 · HTML: https://mlsanity.com/paper/1506.07310/

## Key facts

- Cited by 18 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 24 Jun 2015.

## Built on by (most cited first, 18 of 18)

- [von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning](https://mlsanity.com/paper/2103.15718/) (2021)
- [Face.evoLVe: A High-Performance Face Recognition Library](https://mlsanity.com/paper/2107.08621/) (2021)
- [SphereFace Revived: Unifying Hyperspherical Face Recognition](https://mlsanity.com/paper/2109.05565/) (2021)
- [SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition](https://mlsanity.com/paper/2205.12010/) (2022)
- [Part-based Face Recognition with Vision Transformers](https://mlsanity.com/paper/2212.00057/) (2022)
- [LARNet: Lie Algebra Residual Network for Face Recognition](https://mlsanity.com/paper/2103.08147/) (2021)
- [DiFaReli++: Diffusion Face Relighting with Consistent Cast Shadows](https://mlsanity.com/paper/2304.09479/) (2023)
- [Searching for Alignment in Face Recognition](https://mlsanity.com/paper/2102.05447/) (2021)
- [LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition](https://mlsanity.com/paper/2403.08161/) (2024)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [Locality-aware Channel-wise Dropout for Occluded Face Recognition](https://mlsanity.com/paper/2107.09270/) (2021)
- [Learning Discriminative Representations for Multi-Label Image Recognition](https://mlsanity.com/paper/2107.11159/) (2021)
- [DAIL: Dataset-Aware and Invariant Learning for Face Recognition](https://mlsanity.com/paper/2101.05419/) (2021)
- [A Comprehensive Study on Occlusion Invariant Face Recognition under Face Mask Occlusion](https://mlsanity.com/paper/2201.09089/) (2022)
- [Optimization Can Learn Johnson Lindenstrauss Embeddings](https://mlsanity.com/paper/2412.07242/) (2024)
- [Feature Representation in Deep Metric Embeddings](https://mlsanity.com/paper/2102.03176/) (2021)
- [Deep Learning-based Spatio Temporal Facial Feature Visual Speech Recognition](https://mlsanity.com/paper/2305.00552/) (2023)
- [Attention-based Pin Site Image Classification in Orthopaedic Patients with External Fixators](https://mlsanity.com/paper/2603.24815/) (2026)

## Abstract (arXiv)

Face Recognition has been studied for many decades. As opposed to traditional hand-crafted features such as LBP and HOG, much more sophisticated features can be learned automatically by deep learning methods in a data-driven way. In this paper, we propose a two-stage approach that combines a multi-patch deep CNN and deep metric learning, which extracts low dimensional but very discriminative features for face verification and recognition. Experiments show that this method outperforms other state-of-the-art methods on LFW dataset, achieving 99.77% pair-wise verification accuracy and significantly better accuracy under other two more practical protocols. This paper also discusses the importance of data size and the number of patches, showing a clear path to practical high-performance face recognition systems in real world.