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 The aligned images include ”funneled images” (ICCV 2007), LFW-a, which uses an unpublished method of alignment, and ”deep funneled” images (NIPS 2012).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 48 articles )

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  1. Jun Wang, Yinglu Liu, Yibo Hu, Hailin Shi, Tao Mei: FaceX-Zoo: A PyTorch Toolbox for Face Recognition (2021) arXiv
  2. Amosov, O. S.; Amosova, S. G.; Zhiganov, S. V.; Ivanov, Yu. S.; Pashchenko, F. F.: Computational method for recognizing situations and objects in the frames of a continuous video stream using deep neural networks for access control systems (2020)
  3. Escalante-B., Alberto N.; Wiskott, Laurenz: Improved graph-based SFA: information preservation complements the slowness principle (2020)
  4. Jin, Taisong; Cao, Liujuan; Jie, Feiran; Ji, Rongrong: Link-aware semi-supervised hypergraph (2020)
  5. Likassa, Habte Tadesse: New robust principal component analysis for joint image alignment and recovery via affine transformations, Frobenius and (L_2,1) norms (2020)
  6. Likassa, Habte Tadesse; Xian, Wen; Tang, Xuan: New robust regularized shrinkage regression for high-dimensional image recovery and alignment via affine transformation and Tikhonov regularization (2020)
  7. Valaitis, Vytautas; Marcinkevicius, Virginijus; Jurevicius, Rokas: Learning aerial image similarity using triplet networks (2020)
  8. Görgel, Pelin; Simsek, Ahmet: Face recognition via deep stacked denoising sparse autoencoders (DSDSA) (2019)
  9. Savchenko, A. V.: Sequential three-way decisions in multi-category image recognition with deep features based on distance factor (2019)
  10. Wang, Shuangyue; Xiao, Yunhai; Jin, Zhengfen: An efficient algorithm for batch images alignment with adaptive rank-correction term (2019)
  11. Gao, Wanshun; Zhao, Xi; An, Jun; Zou, Jianhua: Multi-pose 3D facial texture refinement for face recognition (2018)
  12. Gorban, A. N.; Golubkov, A.; Grechuk, B.; Mirkes, E. M.; Tyukin, I. Y.: Correction of AI systems by linear discriminants: probabilistic foundations (2018)
  13. Lock, Eric F.; Li, Gen: Supervised multiway factorization (2018)
  14. Shang, Kun; Huang, Zheng-Hai; Liu, Wanquan; Li, Zhi-Ming: A single gallery-based face recognition using extended joint sparse representation (2018)
  15. Vo, Duc My; Lee, Sang-Woong: Robust face recognition via hierarchical collaborative representation (2018)
  16. Wen, Jie; Fang, Xiaozhao; Xu, Yong; Tian, Chunwei; Fei, Lunke: Low-rank representation with adaptive graph regularization (2018)
  17. Zheng, Charles; Achanta, Rakesh; Benjamini, Yuval: Extrapolating expected accuracies for large multi-class problems (2018)
  18. Cevikalp, Hakan; Triggs, Bill: Visual object detection using cascades of binary and one-class classifiers (2017)
  19. Nakatsukasa, Yuji; Soma, Tasuku; Uschmajew, André: Finding a low-rank basis in a matrix subspace (2017)
  20. Rawat, Waseem; Wang, Zenghui: Deep convolutional neural networks for image classification: a comprehensive review (2017)

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