# DeepID3: Face Recognition with Very Deep Neural Networks

arXiv: https://arxiv.org/abs/1502.00873 · HTML: https://mlsanity.com/paper/1502.00873/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2015.

## Built on by (most cited first, 30 of 62)

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- [PAM: Pose Attention Module for Pose-Invariant Face Recognition](https://mlsanity.com/paper/2111.11940/) (2021)
- [Deep face recognition with clustering based domain adaptation](https://mlsanity.com/paper/2205.13937/) (2022)
- [Open-set Face Recognition using Ensembles trained on Clustered Data](https://mlsanity.com/paper/2308.07445/) (2023)
- [A Multi-task Adversarial Attack Against Face Authentication](https://mlsanity.com/paper/2408.08205/) (2024)
- [TrainVerify: Equivalence-Based Verification for Distributed LLM Training](https://mlsanity.com/paper/2506.15961/) (2025)
- [Inter-class Discrepancy Alignment for Face Recognition](https://mlsanity.com/paper/2103.01559/) (2021)
- [Open-set Face Recognition for Small Galleries Using Siamese Networks](https://mlsanity.com/paper/2105.06967/) (2021)
- [Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency](https://mlsanity.com/paper/2109.12459/) (2021)

## Abstract (arXiv)

The state-of-the-art of face recognition has been significantly advanced by the emergence of deep learning. Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity. This motivates us to investigate their effectiveness on face recognition. This paper proposes two very deep neural network architectures, referred to as DeepID3, for face recognition. These two architectures are rebuilt from stacked convolution and inception layers proposed in VGG net and GoogLeNet to make them suitable to face recognition. Joint face identification-verification supervisory signals are added to both intermediate and final feature extraction layers during training. An ensemble of the proposed two architectures achieves 99.53% LFW face verification accuracy and 96.0% LFW rank-1 face identification accuracy, respectively. A further discussion of LFW face verification result is given in the end.