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 229 articles )

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  1. Diaz-Chito, Katerine; Martínez del Rincón, Jesús; Rusiñol, Marçal; Hernández-Sabaté, Aura: Feature extraction by using dual-generalized discriminative common vectors (2019)
  2. Alim, Affan; Naseem, Imran; Togneri, Roberto; Bennamoun, Mohammed: The most discriminant subbands for face recognition: a novel information-theoretic framework (2018)
  3. Chen, Yudong; Lai, Zhihui; Wen, Jiajun; Gao, Can: Nuclear norm based two-dimensional sparse principal component analysis (2018)
  4. Xie, Ting; Chen, Feiyu: Non-convex clustering via proximal alternating linearized minimization method (2018)
  5. Ren, Jie-Yi; Wu, Xiao-Jun: Vectorial approximations of infinite-dimensional covariance descriptors for image classification (2017)
  6. Zhao, Xiaowei; Nie, Feiping; Wang, Sen; Guo, Jun; Xu, Pengfei; Chen, Xiaojiang: Unsupervised 2D dimensionality reduction with adaptive structure learning (2017)
  7. Cherian, Anoop; Sra, Suvrit: Positive definite matrices: data representation and applications to computer vision (2016)
  8. Harandi, Mehrtash; Basirat, Mina; Lovell, Brian C.: Coordinate coding on the Riemannian manifold of symmetric positive-definite matrices for image classification (2016)
  9. Lahasan, Badr Mohammed; Venkat, Ibrahim; Al-Betar, Mohammed Azmi; Lutfi, Syaheerah Lebai; de Wilde, Philippe: Recognizing faces prone to occlusions and common variations using optimal face subgraphs (2016)
  10. Li, Zhi-Ming; Huang, Zheng-Hai; Zhang, Ting: Gabor-scale binary pattern for face recognition (2016) ioport
  11. Pujol, Francisco A.; Mora, Higinio; Girona-Selva, José A.: A connectionist computational method for face recognition (2016)
  12. Savchenko, A. V.: The maximal likelihood enumeration method for the problem of classifying piecewise regular objects (2016)
  13. Wang, Di; Zhang, Xiaoqin; Fan, Mingyu; Ye, Xiuzi: Hierarchical mixing linear support vector machines for nonlinear classification (2016)
  14. Chen, Wen-Sheng; Dai, Xiuli; Pan, Binbin; Tang, Yuan Yan: Semi-supervised discriminant analysis method for face recognition (2015)
  15. Kamaruzaman, Fadhlan; Shafie, Amir Akramin; Mustafah, Yasir M.: Coincidence detection using spiking neurons with application to face recognition (2015)
  16. 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
  17. Chan, Chi Ho; Kittler, Josef; Poh, Norman: State-of-the-art LBP descriptor for face recognition (2014) ioport
  18. Cheng, Miao; Pun, Chi-Man; Tang, Yuan Yan: Nonnegative class-specific entropy component analysis with adaptive step search criterion (2014) ioport
  19. Chen, Yu; Xu, Xiao-Hong: Supervised orthogonal discriminant subspace projects learning for face recognition (2014)
  20. Gaidhane, Vilas H.; Hote, Yogesh V.; Singh, Vijander: An efficient approach for face recognition based on common eigenvalues (2014) ioport

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