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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Published in | 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 1701 - 1708 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.06.2014
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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. |
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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 |
Author_xml | – sequence: 1 givenname: Yaniv surname: Taigman fullname: Taigman, Yaniv organization: Facebook AI Res., Menlo Park, CA, USA – sequence: 2 surname: Ming Yang fullname: Ming Yang email: mingyang@fb.com organization: Facebook AI Res., Menlo Park, CA, USA – sequence: 3 givenname: Marc'Aurelio surname: Ranzato fullname: Ranzato, Marc'Aurelio email: ranzato@fb.com organization: Facebook AI Res., Menlo Park, CA, USA – sequence: 4 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 Face Face recognition Shape Solid modeling Three-dimensional displays Training |
Title | DeepFace: Closing the Gap to Human-Level Performance in Face Verification |
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