The CMU Pose, Illumination, and Expression (PIE) database. Between October 2000 and December 2000, we collected a database of over 40,000 facial images of 68 people. Using the CMU (Carnegie Mellon University) 3D Room, we imaged each person across 13 different poses, under 43 different illumination conditions, and with four different expressions. We call this database the CMU Pose, Illumination and Expression (PIE) database. In this paper, we describe the imaging hardware, the collection procedure, the organization of the database, several potential uses of the database, and how to obtain the database.

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  1. Huang, Sheng; Yang, Dan; Yongxin, Ge; Zhang, Xiaohong: Combined supervised information with PCA via discriminative component selection (2015)
  2. Qiu, Qiang; Sapiro, Guillermo: Learning transformations for clustering and classification (2015)
  3. Courty, Nicolas; Gong, Xing; Vandel, Jimmy; Burger, Thomas: SAGA: sparse and geometry-aware non-negative matrix factorization through non-linear local embedding (2014)
  4. Draper, Bruce; Kirby, Michael; Marks, Justin; Marrinan, Tim; Peterson, Chris: A flag representation for finite collections of subspaces of mixed dimensions (2014)
  5. Gao, Quanxue; Liu, Jingjing; Cui, Kai; Zhang, Hailin; Wang, Xiaogang: Stable locality sensitive discriminant analysis for image recognition (2014)
  6. Kan, Meina; Wu, Junting; Shan, Shiguang; Chen, Xilin: Domain adaptation for face recognition: targetize source domain bridged by common subspace (2014)
  7. Kavita, Singh; Mukesh, Zaveri; Mukesh, Raghuwanshi: A rough neurocomputing approach for illumination invariant face recognition system (2014)
  8. Li, Sheng; Li, Liangyue; Fu, Yun: Low-rank and sparse dictionary learning (2014)
  9. Ma, Xiaohu; Tan, Yanqi: Face recognition based on discriminant sparsity preserving embedding (2014)
  10. Mehta, Rakesh; Yuan, Jirui; Egiazarian, Karen: Face recognition using scale-adaptive directional and textural features (2014) ioport
  11. Qi, Changping; Gao, Caixia; Fang, Yuan: A domain adaptive learning based on subspace interpolation (2014)
  12. Shao, Ming; Ma, Mingbo; Fu, Yun: Sparse manifold subspace learning (2014)
  13. Wang, Nannan; Tao, Dacheng; Gao, Xinbo; Li, Xuelong; Li, Jie: A comprehensive survey to face hallucination (2014) ioport
  14. Wei, Lai: Optimal subspace learning for sparse representation based classifier via discriminative principal subspaces alignment (2014)
  15. Chen, Xiaobo; Yang, Jian; Zhang, David; Liang, Jun: Complete large margin linear discriminant analysis using mathematical programming approach (2013)
  16. Li, Taiyong; Zhang, Zhilin: Robust face recognition via block sparse Bayesian learning (2013)
  17. Talwalkar, Ameet; Kumar, Sanjiv; Mohri, Mehryar; Rowley, Henry: Large-scale SVD and manifold learning (2013)
  18. Wang, Jianzhong; Wu, Lishan; Kong, Jun; Li, Yuxin; Zhang, Baoxue: Maximum weight and minimum redundancy: a novel framework for feature subset selection (2013)
  19. Wang, Zhifei; Miao, Zhenjiang; Wan, Yanli; Tang Zhen: Kernel coupled cross-regression for low-resolution face recognition (2013)
  20. Zhang, Haichao; Zhang, Yanning; Huang, Thomas S.: Pose-robust face recognition via sparse representation (2013)

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