DeepFace: Closing the Gap to Human-Level Performance in …

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.}

compact face representation, in sheer contrast to the shift toward tens of thousands of appearance features in other re-cent systems [5,7,2]. The proposed system differs from the majority of con-tributions in the field in that it uses the deep learning (DL) framework [3,21] in lieu of well engineered features. DL is

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  Learning, Representation

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