Towards Explainable Person Re- Identification

Visually recognizing an individual in a crowded area using a distributed camera network is essential for a range of biometric and security applications. We propose a shift in perspective of the ongoing re-identification studies, towards creating more explainable and coherent models that are applicable in real-world engineering problems, even if this comes with a slight decrease in performance. The proposed explainable model uses attribute classification to perform the task of re-identification. This method steps away from intrusive and controversial techniques such as facial recognition to improve public acceptance of re-identification models. Current methods of person re-identification do not explain the importance of each attribute in determining the results, and often use complicated and esoteric algorithms to improve the performance on closed-world datasets which may not represent more realistic open-world scenarios. We applied our approach to the Market-1501 dataset and examined the impact of careful selection of backbone outputs for each individual attribute in our experiment. Our simple model is capable of performing attribute classification for 0-shot re-identification that is explainable and less intrusive when compared to state-of-the-art models focused on re-identification.