Evaluating Person Re-identification Performance on GAN-enhanced Datasets

Daniel Hofer, Wolfgang Ertel

Abstract

Person re-identification remains a hard task for AI systems because high intra-class variance across different cameras, angles and lighting conditions make it difficult to create a reliable re-identification system. Since only small datasets for person re-id tasks are available, in recent years Generative Adversarial Networks (GANs) have become popular to improve intra-class variance to train more robust re-identification frameworks. In this work we evaluate an Inception-ResNet-v2 using triplet loss, introduced by (Weinberger and Saul, 2009), which works very well for face re-identification and use it for full-body person re-identification. The network is trained without GAN generated images to get a baseline accuracy of the network. In further experiments, the network is trained by adding constantly rising amounts of synthetic images produced by two image generators using different generating approaches.

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