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.


References in zbMATH (referenced in 210 articles )

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  1. Chen, Yudong; Lai, Zhihui; Wen, Jiajun; Gao, Can: Nuclear norm based two-dimensional sparse principal component analysis (2018)
  2. Xie, Ting; Chen, Feiyu: Non-convex clustering via proximal alternating linearized minimization method (2018)
  3. Cherian, Anoop; Sra, Suvrit: Positive definite matrices: data representation and applications to computer vision (2016)
  4. Harandi, Mehrtash; Basirat, Mina; Lovell, Brian C.: Coordinate coding on the Riemannian manifold of symmetric positive-definite matrices for image classification (2016)
  5. Li, Zhi-Ming; Huang, Zheng-Hai; Zhang, Ting: Gabor-scale binary pattern for face recognition (2016)
  6. Pujol, Francisco A.; Mora, Higinio; Girona-Selva, José A.: A connectionist computational method for face recognition (2016)
  7. Savchenko, A.V.: The maximal likelihood enumeration method for the problem of classifying piecewise regular objects (2016)
  8. Chen, Wen-Sheng; Dai, Xiuli; Pan, Binbin; Tang, Yuan Yan: Semi-supervised discriminant analysis method for face recognition (2015)
  9. Kamaruzaman, Fadhlan; Shafie, Amir Akramin; Mustafah, Yasir M.: Coincidence detection using spiking neurons with application to face recognition (2015)
  10. 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
  11. Chan, Chi Ho; Kittler, Josef; Poh, Norman: State-of-the-art LBP descriptor for face recognition (2014) ioport
  12. Cheng, Miao; Pun, Chi-Man; Tang, Yuan Yan: Nonnegative class-specific entropy component analysis with adaptive step search criterion (2014) ioport
  13. Chen, Yu; Xu, Xiao-Hong: Supervised orthogonal discriminant subspace projects learning for face recognition (2014)
  14. Gaidhane, Vilas H.; Hote, Yogesh V.; Singh, Vijander: An efficient approach for face recognition based on common eigenvalues (2014) ioport
  15. Hernandez, John A.Ruiz; Crowley, James L.; Lux, Augustin; Pietikäinen, Matti: Histogram-tensorial Gaussian representations and its applications to facial analysis (2014) ioport
  16. Kang, Jeonil; Nyang, DaeHun; Lee, KyungHee: Two-factor face authentication using matrix permutation transformation and a user password (2014) ioport
  17. Karczmarek, Paweł; Pedrycz, Witold; Reformat, Marek; Akhoundi, Elaheh: A study in facial regions saliency: a fuzzy measure approach (2014) ioport
  18. Li, Yongchao; Cai, Cheng; Qiu, Guoping; Lam, Kin-Man: Face hallucination based on sparse local-pixel structure (2014) ioport
  19. Li, Yuelong; Feng, Jufu; Meng, Li; Wu, Jigang: Sparse representation shape models (2014) ioport
  20. Ma, Andy J.; Yuen, Pong C.: Reduced analytic dependency modeling: robust fusion for visual recognition (2014)

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