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DeepFace: Closing the Gap to Human-Level Performance in Face VerificationYaniv TaigmanMing YangMarc Aurelio RanzatoFacebook AI ResearchMenlo Park, CA, USA{yaniv, mingyang, WolfTel Aviv UniversityTel Aviv, modern face recognition, the conventional pipelineconsists of four stages: detect align represent clas-sify. We revisit both the alignment step and the representa-tion step by employing explicit 3D face modeling in order toapply a piecewise affine transformation, and derive a facerepresentation from a nine-layer deep neural network. Thisdeep network involves more than 120 million parametersusing several locally connected layers without weight shar-ing, rather than the standard convolutional layers. Thuswe trained it on the largest facial dataset to-date, an iden-tity labeled dataset of four million facial images belong-ing to more than 4,000 identities. The learned representa-tions coupling the accurate model-based alignment with thelarge facial database generalize remarkably well to faces inunconstrained environments, even with a simple method reaches an accuracy of on the LabeledFaces in the Wild (LFW) dataset, reducing the error of thecurrent state of the art by more than 27%, closely approach-ing Human-Level IntroductionFace recognition in unconstrained images is at the fore-front of the algorithmic perception revolution.}
DeepFace: Closing the Gap to Human-Level Performance in Face Verification Yaniv Taigman Ming Yang Marc’Aurelio Ranzato Facebook AI Research Menlo Park, CA, USA
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