FERET

The FERET database and evaluation procedure for face-recognition algorithms. The Face Recognition Technology (FERET) program database is a large database of facial images, divided into development and sequestered portions. The development portion is made available to researchers, and the sequestered portion is reserved for testing facerecognition algorithms. The FERET evaluation procedure is an independently administered test of face-recognition algorithms. The test was designed to: (1) allow a direct comparison between different algorithms, (2) identify the most promising approaches, (3) assess the state of the art in face recognition, (4) identify future directions of research, and (5) advance the state of the art in face recognition.

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References in zbMATH (referenced in 234 articles )

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  1. Chan, Chi Ho; Kittler, Josef; Poh, Norman: State-of-the-art LBP descriptor for face recognition (2014) ioport
  2. Cheng, Miao; Pun, Chi-Man; Tang, Yuan Yan: Nonnegative class-specific entropy component analysis with adaptive step search criterion (2014) ioport
  3. Chen, Yu; Xu, Xiao-Hong: Supervised orthogonal discriminant subspace projects learning for face recognition (2014)
  4. Gaidhane, Vilas H.; Hote, Yogesh V.; Singh, Vijander: An efficient approach for face recognition based on common eigenvalues (2014) ioport
  5. Hernandez, John A. Ruiz; Crowley, James L.; Lux, Augustin; Pietikäinen, Matti: Histogram-tensorial Gaussian representations and its applications to facial analysis (2014) ioport
  6. Jin, Bo; Jing, Zhongliang; Zhao, Haitao: EVD dualdating based online subspace learning (2014)
  7. Kang, Jeonil; Nyang, DaeHun; Lee, KyungHee: Two-factor face authentication using matrix permutation transformation and a user password (2014) ioport
  8. Karczmarek, Paweł; Pedrycz, Witold; Reformat, Marek; Akhoundi, Elaheh: A study in facial regions saliency: a fuzzy measure approach (2014) ioport
  9. Li, Yongchao; Cai, Cheng; Qiu, Guoping; Lam, Kin-Man: Face hallucination based on sparse local-pixel structure (2014) ioport
  10. Li, Yuelong; Feng, Jufu; Meng, Li; Wu, Jigang: Sparse representation shape models (2014) ioport
  11. Ma, Andy J.; Yuen, Pong C.: Reduced analytic dependency modeling: robust fusion for visual recognition (2014)
  12. Mehta, Rakesh; Yuan, Jirui; Egiazarian, Karen: Face recognition using scale-adaptive directional and textural features (2014) ioport
  13. Mohamad AL-Shiha, Abeer A.; Woo, W. L.; Dlay, S. S.: Multi-linear neighborhood preserving projection for face recognition (2014)
  14. Tang, Y. Y.; Xia, Tian; Wei, Yantao; Li, Hong; Li, Luoqing: Hierarchical kernel-based rotation and scale invariant similarity (2014) ioport
  15. Uddin, Md. Zia: An efficient local feature-based facial expression recognition system (2014) ioport
  16. Wang, Nannan; Tao, Dacheng; Gao, Xinbo; Li, Xuelong; Li, Jie: A comprehensive survey to face hallucination (2014) ioport
  17. Yu, Zhe-Zhou; Liu, Yu-Hao; Li, Bin; Pang, Shu-Chao; Jia, Cheng-Cheng: Incremental graph regulated nonnegative matrix factorization for face recognition (2014)
  18. Zhao, Haitao; Wong, W. K.: Regularized discriminant entropy analysis (2014)
  19. Chen, Wen-Sheng; Zhang, Chu; Chen, Shengyong: Geometric distribution weight information modeled using radial basis function with fractional order for linear discriminant analysis method (2013)
  20. Chen, Yu; Zheng, Wei-Shi; Xu, Xiao-Hong; Lai, Jian-Huang: Discriminant subspace learning constrained by locally statistical uncorrelation for face recognition (2013)

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