# Triplet Probabilistic Embedding for Face Verification and Clustering

arXiv: https://arxiv.org/abs/1604.05417 · HTML: https://mlsanity.com/paper/1604.05417/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2016.

## Built on by (most cited first, 14 of 14)

- [PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition](https://mlsanity.com/paper/2108.03764/) (2021)
- [LARNet: Lie Algebra Residual Network for Face Recognition](https://mlsanity.com/paper/2103.08147/) (2021)
- [PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition](https://mlsanity.com/paper/2107.11721/) (2021)
- [Distill and De-bias: Mitigating Bias in Face Verification using Knowledge Distillation](https://mlsanity.com/paper/2112.09786/) (2021)
- [Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss](https://mlsanity.com/paper/2311.00400/) (2023)
- [VIGFace: Virtual Identity Generation for Privacy-Free Face Recognition](https://mlsanity.com/paper/2403.08277/) (2024)
- [Attributes Shape the Embedding Space of Face Recognition Models](https://mlsanity.com/paper/2507.11372/) (2025)
- [Pose Attention-Guided Profile-to-Frontal Face Recognition](https://mlsanity.com/paper/2209.07001/) (2022)
- [Template-based Multi-Domain Face Recognition](https://mlsanity.com/paper/2409.09832/) (2024)
- [Quality-Aware Multimodal Biometric Recognition](https://mlsanity.com/paper/2112.05827/) (2021)
- [Active metric learning and classification using similarity queries](https://mlsanity.com/paper/2202.01953/) (2022)
- [Modeling biological face recognition with deep convolutional neural networks](https://mlsanity.com/paper/2208.06681/) (2022)
- [SubFace: Learning with Softmax Approximation for Face Recognition](https://mlsanity.com/paper/2208.11483/) (2022)
- [A Survey on Face Recognition Systems](https://mlsanity.com/paper/2201.02991/) (2022)

## Abstract (arXiv)

Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem. This paper proposes an approach that couples a deep CNN-based approach with a low-dimensional discriminative embedding learned using triplet probability constraints to solve the unconstrained face verification problem. Aside from yielding performance improvements, this embedding provides significant advantages in terms of memory and for post-processing operations like subject specific clustering. Experiments on the challenging IJB-A dataset show that the proposed algorithm performs comparably or better than the state of the art methods in verification and identification metrics, while requiring much less training data and training time. The superior performance of the proposed method on the CFP dataset shows that the representation learned by our deep CNN is robust to extreme pose variation. Furthermore, we demonstrate the robustness of the deep features to challenges including age, pose, blur and clutter by performing simple clustering experiments on both IJB-A and LFW datasets.