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CenterFace: Face as Point

介绍

实用的边缘设备无锚人脸检测与对齐算法Centerface, 模型大小7.3M。 CenterFace-small 性能达到centerface的同时模型大小仅为2.3M。

image

运行环境

  • OpenCV 4.1.0
  • Numpy
  • Python3.6+

准确性

  • WIDER FACE val集结果:
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 0.887 0.870 0.792
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
  • WIDER FACE test集结果:
Model Version Easy Set Medium Set Hard Set
FaceBoxes 0.839 0.763 0.396
FaceBoxes3.2× 0.791 0.794 0.715
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 是RetinaFace-MobileNet-0.25的简写,来自于非常好的工作insightface
  • LFFD-v1 也是很好的工作LFFD
  • CenterFace/CenterFace-small的测试方法是MULTI-SCALE,因为训练图像和测试图像尺度的不一致性,多尺度测试才能反应centerface的真实性能。 不过,对于SIO(原图单次推理),CenterFace在val集上也可以达到:92.2% (Easy), 91.1% (Medium) and 78.2% 。 而RetinaFace-mnet在val集上是:89.6% (Easy), 87.1% (Medium) and 68.1%
  • FDDB的结果:
Model Version Disc ROC curves score
RetinaFace-mnet 96.0@1000
LFFD-v1 97.3@1000
LFFD-v2 97.2@1000
CenterFace 98.0@1000
CenterFace-small 98.1@1000

推理速度

  • NVIDIA RTX 2080TI推理耗时:
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

image

image

image

Discussion

欢迎加入 QQ Group(912759877) 交流讨论, 包括但不限:人脸检测、稠密对齐、活体、3D重建等。

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  • Python 100.0%