# Person Re-identification with Bias-controlled Adversarial Training

## TL;DR

This paper introduces a Bias-controlled Adversarial framework (BCA) for person re-identification that effectively manages biases like pose, body part, and camera view, improving accuracy across various scenarios.

## Contribution

It presents a novel adversarial training approach that controls bias features, enhancing person re-ID performance beyond existing methods.

## Key findings

- Improves re-ID accuracy on multiple benchmarks.
- Effective in full and partial view scenarios.
- Outperforms state-of-the-art methods.

## Abstract

Inspired by the effectiveness of adversarial training in the area of Generative Adversarial Networks we present a new approach for learning feature representations in person re-identification. We investigate different types of bias that typically occur in re-ID scenarios, i.e., pose, body part and camera view, and propose a general approach to address them. We introduce an adversarial strategy for controlling bias, named Bias-controlled Adversarial framework (BCA), with two complementary branches to reduce or to enhance bias-related features. The results and comparison to the state of the art on different benchmarks show that our framework is an effective strategy for person re-identification. The performance improvements are in both full and partial views of persons.