Multi-PIE

Multi-PIE: A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner. The CMU PIE database has been very influential in advancing research in face recognition across pose and illumination. Despite its success the PIE database has several shortcomings: a limited number of subjects, a single recording session and only few expressions captured. To address these issues we collected the CMU Multi-PIE database. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. In this paper we introduce the database and describe the recording procedure. We furthermore present results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.


References in zbMATH (referenced in 25 articles )

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  1. Wu, Ying Nian; Gao, Ruiqi; Han, Tian; Zhu, Song-Chun: A tale of three probabilistic families: discriminative, descriptive, and generative models (2019)
  2. Lin, Guojun; Yang, Meng; Shen, Linlin; Yang, Mingzhong; Xie, Mei: Robust and discriminative dictionary learning for face recognition (2018)
  3. Öfjäll, Kristoffer; Felsberg, Michael: Approximative coding methods for channel representations (2018)
  4. Shang, Kun; Huang, Zheng-Hai; Liu, Wanquan; Li, Zhi-Ming: A single gallery-based face recognition using extended joint sparse representation (2018)
  5. Sankaran, Anush; Goswami, Gaurav; Vatsa, Mayank; Singh, Richa; Majumdar, Angshul: Class sparsity signature based restricted Boltzmann machine (2017)
  6. Zhang, He; Patel, Vishal M.: Discriminative sparse representations (2017)
  7. Hovhannisyan, Vahan; Parpas, Panos; Zafeiriou, Stefanos: MAGMA: multilevel accelerated gradient mirror descent algorithm for large-scale convex composite minimization (2016)
  8. Zhou, Zhaoze; Zheng, Wei-Shi; Hu, Jian-Fang; Xu, Yong; You, Jane: One-pass online learning: a local approach (2016)
  9. Cament, Leonardo A.; Castillo, Luis E.; Perez, Juan P.; Galdames, Francisco J.; Perez, Claudio A.: Fusion of local normalization and Gabor entropy weighted features for face identification (2014) ioport
  10. Igual, Laura; Perez-Sala, Xavier; Escalera, Sergio; Angulo, Cecilio; De la Torre, Fernando: Continuous generalized Proceustes analysis (2014)
  11. Kan, Meina; Wu, Junting; Shan, Shiguang; Chen, Xilin: Domain adaptation for face recognition: targetize source domain bridged by common subspace (2014)
  12. Peng, Xi; Zhang, Lei; Yi, Zhang; Tan, Kok Kiong: Learning locality-constrained collaborative representation for robust face recognition (2014) ioport
  13. Wang, Donghui; Kong, Shu: A classification-oriented dictionary learning model: explicitly learning the particularity and commonality across categories (2014)
  14. Yang, Meng; Feng, Zhizhao; Shiu, Simon C. K.; Zhang, Lei: Fast and robust face recognition via coding residual map learning based adaptive masking (2014)
  15. Yang, Meng; Zhang, Lei; Feng, Xiangchu; Zhang, David: Sparse representation based Fisher discrimination dictionary learning for image classification (2014)
  16. Feng, Zhizhao; Yang, Meng; Zhang, Lei; Liu, Yan; Zhang, David: Joint discriminative dimensionality reduction and dictionary learning for face recognition (2013) ioport
  17. Yang, Meng; Zhang, Lei; Shiu, Simon C. K.; Zhang, David: Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary (2013) ioport
  18. Lei, Zhen; Zhang, Zhiwei; Li, Stan Z.: Feature space locality constraint for kernel based nonlinear discriminant analysis (2012)
  19. Tunç, Birkan; Dağlı, Volkan; Gökmen, Muhittin: Class dependent factor analysis and its application to face recognition (2012)
  20. Zhang, Haichao; Nasrabadi, Nasser M.; Zhang, Yanning; Huang, Thomas S.: Joint dynamic sparse representation for multi-view face recognition (2012) ioport

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