Deepface: Closing the gap to human-level performance in face verification. In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

References in zbMATH (referenced in 27 articles )

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  1. Janson, N. B.; Kloeden, P. E.: Mathematical consistency and long-term behaviour of a dynamical system with a self-organising vector field (2022)
  2. Ghaffarian, Seyed Mohammad; Shahriari, Hamid Reza: Neural software vulnerability analysis using rich intermediate graph representations of programs (2021)
  3. Ghods, Alireza; Cook, Diane J.: A survey of deep network techniques all classifiers can adopt (2021)
  4. Qingzhong Wang, Pengfei Zhang, Haoyi Xiong, Jian Zhao: Face.evoLVe: A High-Performance Face Recognition Library (2021) arXiv
  5. Wäldchen, Stephan; Macdonald, Jan; Hauch, Sascha; Kutyniok, Gitta: The computational complexity of understanding binary classifier decisions (2021)
  6. Yu, Jiahui; Spiliopoulos, Konstantinos: Normalization effects on shallow neural networks and related asymptotic expansions (2021)
  7. Duan, Jia; Zhou, Jiantao; Li, Yuanman: Privacy-preserving distributed deep learning based on secret sharing (2020)
  8. Liu, Minliang; Liang, Liang; Sun, Wei: A generic physics-informed neural network-based constitutive model for soft biological tissues (2020)
  9. Pai, Gautam; Joseph-Rivlin, Mor; Kimmel, Ron; Sochen, Nir: On geometric invariants, learning, and recognition of shapes and forms (2020)
  10. Sirignano, Justin; Spiliopoulos, Konstantinos: Mean field analysis of neural networks: a law of large numbers (2020)
  11. Ghatak, Abhijit: Deep learning with R (2019)
  12. Görgel, Pelin; Simsek, Ahmet: Face recognition via deep stacked denoising sparse autoencoders (DSDSA) (2019)
  13. He, Lingxiao; Li, Haiqing; Zhang, Qi; Sun, Zhenan: Dynamic feature matching for partial face recognition (2019)
  14. Liu, Minliang; Liang, Liang; Sun, Wei: Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach (2019)
  15. Viroli, Cinzia; McLachlan, Geoffrey J.: Deep Gaussian mixture models (2019)
  16. Wang, Shanshan; Chen, Ying: A joint loss function for deep face recognition (2019)
  17. Xue, Shan; Zhu, Hong: Low-resolution and open-set face recognition via recursive label propagation based on statistical classification (2019)
  18. Dong, Qiulei; Wang, Hong; Hu, Zhanyi: Statistics of visual responses to image object stimuli from primate AIT neurons to DNN neurons (2018)
  19. Zhu, Xudong; Li, Hui; Yu, Yang: Blockchain-based privacy preserving deep learning (2018)
  20. Rawat, Waseem; Wang, Zenghui: Deep convolutional neural networks for image classification: a comprehensive review (2017)

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