Pose Invariant Person Re-Identification using Robust Pose-transformation GAN

TL;DR

This paper introduces a pose-invariant person re-identification method using a GAN-based framework that generates images in various poses, clusters poses for discriminative feature extraction, and fuses features to improve robustness against pose variations and occlusions.

Contribution

The paper presents a novel re-ID pipeline combining pose transformation GAN, pose clustering, and feature fusion to achieve robust, viewpoint-invariant person re-identification.

Findings

Outperforms state-of-the-art GAN-based re-ID models on four benchmarks.

Achieves higher re-ID accuracy than existing models with pose variation handling.

Robust to occlusion, scale, rotation, and illumination changes.

Abstract

The objective of person re-identification (re-ID) is to retrieve a person's images from an image gallery, given a single instance of the person of interest. Despite several advancements, learning discriminative identity-sensitive and viewpoint invariant features for robust Person Re-identification is a major challenge owing to the large pose variation of humans. This paper proposes a re-ID pipeline that utilizes the image generation capability of Generative Adversarial Networks combined with pose clustering and feature fusion to achieve pose invariant feature learning. The objective is to model a given person under different viewpoints and large pose changes and extract the most discriminative features from all the appearances. The pose transformational GAN (pt-GAN) module is trained to generate a person's image in any given pose. In order to identify the most significant poses for discriminative feature extraction, a Pose Clustering module is proposed. The given instance of the person is modelled in varying poses and these features are effectively combined through the Feature Fusion Network. The final re-ID model consisting of these 3 sub-blocks, alleviates the pose dependence in person re-ID. Also, The proposed model is robust to occlusion, scale, rotation and illumination, providing a framework for viewpoint invariant feature learning. The proposed method outperforms the state-of-the-art GAN based models in 4 benchmark datasets. It also surpasses the state-of-the-art models that report higher re-ID accuracy in terms of improvement over baseline.