SeesawFaceNets: sparse and robust face verification model for mobile platform

arXiv 2019 Jintao Zhang

Deep Convolutional Neural Network (DCNNs) come to be the most widely used solution for most computer vision related tasks, and one of the most important application scenes is face verification. Due to its high-accuracy performance, deep face verification models of which the inference stage occurs on cloud platform through internet plays the key role on most prectical scenes... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Face Verification AgeDB-30 Seesaw-shuffleFaceNet Accuracy 0.9685 # 3
Face Verification CFP-FP Seesaw-shuffleFaceNet (mobi) Accuracy 0.9307 # 2
Face Verification Labeled Faces in the Wild Seesaw-shuffleFaceNet (mobi) Accuracy 99.65% # 7

Methods used in the Paper


METHOD TYPE
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