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 fa...

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Veröffentlicht in:2014 IEEE Conference on Computer Vision and Pattern Recognition S. 1701 - 1708
Hauptverfasser: Taigman, Yaniv, Ming Yang, Ranzato, Marc'Aurelio, Wolf, Lior
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2014
Schlagworte:
Agriculture
Alignment
Computer vision
Face
Face recognition
Facial
Networks
Neural networks
Pattern recognition
Representations
Shape
Solid modeling
State of the art
Three-dimensional displays
Training
ISSN:1063-6919, 1063-6919
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Abstract 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.
AbstractList 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.
Author Ranzato, Marc'Aurelio
Taigman, Yaniv
Ming Yang
Wolf, Lior
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  givenname: Yaniv
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  surname: Ming Yang
  fullname: Ming Yang
  email: mingyang@fb.com
  organization: Facebook AI Res., Menlo Park, CA, USA
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  givenname: Marc'Aurelio
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  givenname: Lior
  surname: Wolf
  fullname: Wolf, Lior
  email: wolf@cs.tau.ac.il
  organization: Tel Aviv Univ., Tel Aviv, Israel
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Snippet In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and...
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SubjectTerms Agriculture
Alignment
Computer vision
Face
Face recognition
Facial
Networks
Neural networks
Pattern recognition
Representations
Shape
Solid modeling
State of the art
Three-dimensional displays
Training
Title DeepFace: Closing the Gap to Human-Level Performance in Face Verification
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