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A practical anchor-free face detection and alignment method for edge devices

A practical anchor-free face detection and alignment method for edge devices

CenterFace

CenterFace(size of 7.3MB) is a practical anchor-free face detection and alignment method for edge devices.

Recent Update

  • 2019.09.13 CenterFace is released.

Environment

  • OpenCV 4.1.0
  • Numpy
  • Python3.6+

Accuracy

  • Results on val set of WIDER FACE:
Model Version Easy Set Medium Set Hard Set
FaceBoxes 0.840 0.766 0.395
FaceBoxes3.2× 0.798 0.802 0.715
RetinaFace-mnet 0.896 0.871 0.681
LFFD-v1 0.910 0.881 0.780
LFFD-v2 0.837 0.835 0.729
CenterFace 0.935 0.924 0.875
CenterFace-small 0.931 0.924 0.870
  • Results on test set of WIDER FACE:
Model Version Easy Set Medium Set Hard Set
FaceBoxes 0.839 0.763 0.396
FaceBoxes3.2× 0.791 0.794 0.715
RetinaFace-mnet 0.896 0.871 0.681
LFFD-v1 0.910 0.881 0.780
LFFD-v2 0.837 0.835 0.729
CenterFace 0.932 0.921 0.873
  • RetinaFace-mnet is short for RetinaFace-MobileNet-0.25 from excellent work insightface.
  • LFFD-v1 is from prefect work LFFD.
  • CenterFace/CenterFace-small evaluation is under MULTI-SCALE, FLIP.
  • For SIO(Single Inference on the Original) evaluation schema, CenterFace also produces 92.2% (Easy), 91.1% (Medium) and 78.2% (Hard) for validation set.
  • Results on FDDB:
Model Version Disc ROC curves score
RetinaFace-mnet [email protected]
LFFD-v1 [email protected]
LFFD-v2 [email protected]
CenterFace [email protected]
CenterFace-small [email protected]

Inference Latency

  • Latency on NVIDIA RTX 2080TI:
Resolution-> 640×480 1280×720(704) 1920×1080(1056)
RetinaFace-mnet 5.40ms 6.31ms 10.26ms
LFFD-v1 7.24ms 14.58ms 28.36ms
CenterFace 5.5ms 6.4ms 8.7ms
CenterFace-small 4.4ms 5.7ms 7.3ms

Results: Face as Point

box_lm

bl3

bl1

GitHub

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