  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.
