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Code for CVPR17 Pose-Aware Person Recognition.
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README.md

README.md

Person recognition

This repository contains the implementation of paper "Pose-Aware Person Recognition" by Vijay Kumar, Anoop Namboodiri, Manohar Paluri, C V Jawahar published at CVPR17.

The implementation is based on Python Caffe.

Datasets:

  1. Download the datasets from the below links and place in data/ folder.
  2. PIPA (test): Link
  3. Hannah movie : Link
  4. IMDB : Link
  5. Soccer videos : Link

Models:

  1. Download the trained models and place in models/ folder.
  2. The models (baseline, pose-specific and pose estimator) are available at link

Testing:

Dependencies: Liblinear.

  1. To reproduce the results on PIPA test set, run run_PIPA.ipynb
  2. For recognition in movie scenario, run run_hannah.ipynb
  3. For recognition in soccer setting, run run_soccer.ipynb
  4. Change the data folder variable in these scripts according to your path.
  5. Replace the liblinear path to your correct liblinear installation directory.

References:

If you use this code or data, please cite the following papers.

  1. Vijay Kumar, Anoop Namboodiri, Manohar Paluri, C V Jawahar, Pose-Aware Person Recognition, CVPR 2017.
  2. N. Zhang et al., Beyond Fronta Faces: Improving Person Recognition using Multiple Cues, CVPR 2014.
  3. Oh et al., Person Recognition in Personal Photo Collections, ICCV 2015.
  4. Li et al., A Multi-lvel Contextual Model for Person Recognition in Photo Albums, CVPR 2016.
  5. Ozerov et al., On Evaluating Face Tracks in Movies, ICIP 2013.
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