DeepFace: Closing the Gap to Human-Level Performance in Face Verification

@article{Taigman2014DeepFaceCT,
  title={DeepFace: Closing the Gap to Human-Level Performance in Face Verification},
  author={Yaniv Taigman and Ming Yang and Marc'Aurelio Ranzato and Lior Wolf},
  journal={2014 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2014},
  pages={1701-1708}
}
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… CONTINUE READING

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