Person Recognition in Personal Photo Collections
This addresses the problem of identifying individuals in everyday photos for applications like social media and personal organization, but it is incremental as it builds on existing methods with specific improvements.
The paper tackles person recognition in personal photo collections by proposing a convnet-based system, analyzing body cues and training data impact, and improving state-of-the-art results on the PIPA dataset.
Recognising persons in everyday photos presents major challenges (occluded faces, different clothing, locations, etc.) for machine vision. We propose a convnet based person recognition system on which we provide an in-depth analysis of informativeness of different body cues, impact of training data, and the common failure modes of the system. In addition, we discuss the limitations of existing benchmarks and propose more challenging ones. Our method is simple and is built on open source and open data, yet it improves the state of the art results on a large dataset of social media photos (PIPA).