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Code for CVPR17 Pose-Aware Person Recognition.
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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.


  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


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


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.


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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