# Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition

arXiv: https://arxiv.org/abs/2012.09571 · HTML: https://mlsanity.com/paper/2012.09571/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Robust Physical-World Attacks on Face Recognition](https://mlsanity.com/paper/2109.09320/) (2021)
- [Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection](https://mlsanity.com/paper/2409.04796/) (2024)
- [Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach](https://mlsanity.com/paper/2404.18101/) (2024)
- [Managing the unknown: a survey on Open Set Recognition and tangential areas](https://mlsanity.com/paper/2312.08785/) (2023)
- [Synthetic Non-stationary Data Streams for Recognition of the Unknown](https://mlsanity.com/paper/2505.13745/) (2025)
- [CLFace: A Scalable and Resource-Efficient Continual Learning Framework for Lifelong Face Recognition](https://mlsanity.com/paper/2411.13886/) (2024)
- [A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution](https://mlsanity.com/paper/2309.13940/) (2023)
- [CalFuse: Multi-Modal Continual Learning via Feature Calibration and Parameter Fusion](https://mlsanity.com/paper/2503.18672/) (2025)

## Abstract (arXiv)

Deep Learning approaches have brought solutions, with impressive performance, to general classification problems where wealthy of annotated data are provided for training. In contrast, less progress has been made in continual learning of a set of non-stationary classes, mainly when applied to unsupervised problems with streaming data. Here, we propose a novel incremental learning approach which combines a deep features encoder with an Open-Set Dynamic Ensembles of SVM, to tackle the problem of identifying individuals of interest (IoI) from streaming face data. From a simple weak classifier trained on a few video-frames, our method can use unsupervised operational data to enhance recognition. Our approach adapts to new patterns avoiding catastrophic forgetting and partially heals itself from miss-adaptation. Besides, to better comply with real world conditions, the system was designed to operate in an open-set setting. Results show a benefit of up to 15% F1-score increase respect to non-adaptive state-of-the-art methods.