LFW

LFW database - Labeled Faces in the Wild. Welcome to Labeled Faces in the Wild, a database of face photographs designed for studying the problem of unconstrained face recognition. The data set contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person pictured. 1680 of the people pictured have two or more distinct photos in the data set. The only constraint on these faces is that they were detected by the Viola-Jones face detector. More details can be found in the technical report below. There are now four different sets of LFW images including the original and three different types of ”aligned” images. The aligned images include ”funneled images” (ICCV 2007), LFW-a, which uses an unpublished method of alignment, and ”deep funneled” images (NIPS 2012). Among these, LFW-a and the deep funneled images produce superior results for most face verification algorithms over the original images and over the funneled images (ICCV 2007).


References in zbMATH (referenced in 41 articles )

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  1. Escalante-B., Alberto N.; Wiskott, Laurenz: Improved graph-based SFA: information preservation complements the slowness principle (2020)
  2. Likassa, Habte Tadesse: New robust principal component analysis for joint image alignment and recovery via affine transformations, Frobenius and (L_2,1) norms (2020)
  3. Ul Rahman, Jamshaid; Chen, Qing; Yang, Zhouwang: Additive parameter for deep face recognition (2020)
  4. Görgel, Pelin; Simsek, Ahmet: Face recognition via deep stacked denoising sparse autoencoders (DSDSA) (2019)
  5. Wang, Shuangyue; Xiao, Yunhai; Jin, Zhengfen: An efficient algorithm for batch images alignment with adaptive rank-correction term (2019)
  6. Gao, Wanshun; Zhao, Xi; An, Jun; Zou, Jianhua: Multi-pose 3D facial texture refinement for face recognition (2018)
  7. Lock, Eric F.; Li, Gen: Supervised multiway factorization (2018)
  8. Shang, Kun; Huang, Zheng-Hai; Liu, Wanquan; Li, Zhi-Ming: A single gallery-based face recognition using extended joint sparse representation (2018)
  9. Wen, Jie; Fang, Xiaozhao; Xu, Yong; Tian, Chunwei; Fei, Lunke: Low-rank representation with adaptive graph regularization (2018)
  10. Xie, Hao; Du, Yunyan; Yu, Huapeng; Chang, Yongxin; Xu, Zhiyong; Tang, Yuanyan: Open set face recognition with deep transfer learning and extreme value statistics (2018)
  11. Zheng, Charles; Achanta, Rakesh; Benjamini, Yuval: Extrapolating expected accuracies for large multi-class problems (2018)
  12. Nakatsukasa, Yuji; Soma, Tasuku; Uschmajew, André: Finding a low-rank basis in a matrix subspace (2017)
  13. Rawat, Waseem; Wang, Zenghui: Deep convolutional neural networks for image classification: a comprehensive review (2017)
  14. Antoniuk, Kostiantyn; Franc, Vojtěch; Hlaváč, Václav: V-shaped interval insensitive loss for ordinal classification (2016)
  15. Hovhannisyan, Vahan; Parpas, Panos; Zafeiriou, Stefanos: MAGMA: multilevel accelerated gradient mirror descent algorithm for large-scale convex composite minimization (2016)
  16. Li, Zhi-Ming; Huang, Zheng-Hai; Zhang, Ting: Gabor-scale binary pattern for face recognition (2016) ioport
  17. Pujol, Francisco A.; Mora, Higinio; Girona-Selva, José A.: A connectionist computational method for face recognition (2016)
  18. Zhang, Le; Suganthan, P. N.: A survey of randomized algorithms for training neural networks (2016)
  19. Benitez-Quiroz, C. F.; Rivera, Samuel; Gotardo, Paulo F. U.; Martinez, Aleix M.: Salient and non-salient fiducial detection using a probabilistic graphical model (2014) ioport
  20. Cao, Xudong; Wei, Yichen; Wen, Fang; Sun, Jian: Face alignment by explicit shape regression (2014) ioport

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