FaceNet: A Unified Embedding for Face Recognition and Clustering

CVPR 2015 Florian SchroffDmitry KalenichenkoJames Philbin

Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Face Verification IJB-C FaceNet TAR @ FAR=0.01 66.50% # 5
Face Verification Labeled Faces in the Wild FaceNet Accuracy 99.63% # 7
Face Identification MegaFace FaceNet Accuracy 70.49% # 7
Face Verification MegaFace FaceNet Accuracy 86.47% # 7
Face Verification YouTube Faces DB FaceNet Accuracy 95.12% # 8

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Face Verification CK+ FaceNet Accuracy 98.00 # 2
Face Verification Oulu-CASIA FaceNet Accuracy 97.50 # 2

Methods used in the Paper


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