# CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval

arXiv: https://arxiv.org/abs/1604.02975 · HTML: https://mlsanity.com/paper/1604.02975/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2016.

## Built on by (most cited first, 4 of 4)

- [Decentralized Multi-Task Online Convex Optimization Under Random Link Failures](https://mlsanity.com/paper/2401.02011/) (2024)
- [IDEAL: Independent Domain Embedding Augmentation Learning](https://mlsanity.com/paper/2105.10112/) (2021)
- [FaIRCoP: Facial Image Retrieval using Contrastive Personalization](https://mlsanity.com/paper/2205.15870/) (2022)
- [Online Decentralized Federated Multi-task Learning With Trustworthiness in Cyber-Physical Systems](https://mlsanity.com/paper/2509.00992/) (2025)

## Abstract (arXiv)

We propose a novel Coupled Projection multi-task Metric Learning (CP-mtML) method for large scale face retrieval. In contrast to previous works which were limited to low dimensional features and small datasets, the proposed method scales to large datasets with high dimensional face descriptors. It utilises pairwise (dis-)similarity constraints as supervision and hence does not require exhaustive class annotation for every training image. While, traditionally, multi-task learning methods have been validated on same dataset but different tasks, we work on the more challenging setting with heterogeneous datasets and different tasks. We show empirical validation on multiple face image datasets of different facial traits, e.g. identity, age and expression. We use classic Local Binary Pattern (LBP) descriptors along with the recent Deep Convolutional Neural Network (CNN) features. The experiments clearly demonstrate the scalability and improved performance of the proposed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.