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the300w_lp

  • Description:

300W-LP Dataset is expanded from 300W, which standardises multiple alignment databases with 68 landmarks, including AFW, LFPW, HELEN, IBUG and XM2VTS. With 300W, 300W-LP adopt the proposed face profiling to generate 61,225 samples across large poses (1,786 from IBUG, 5,207 from AFW, 16,556 from LFPW and 37,676 from HELEN, XM2VTS is not used).

The dataset can be employed as the training set for the following computer vision tasks: face attribute recognition and landmark (or facial part) locaization.

Split Examples
'train' 61,225
  • Feature structure:
FeaturesDict({
    'color_params': Tensor(shape=(7,), dtype=tf.float32),
    'exp_params': Tensor(shape=(29,), dtype=tf.float32),
    'illum_params': Tensor(shape=(10,), dtype=tf.float32),
    'image': Image(shape=(450, 450, 3), dtype=tf.uint8),
    'landmarks_2d': Tensor(shape=(68, 2), dtype=tf.float32),
    'landmarks_3d': Tensor(shape=(68, 2), dtype=tf.float32),
    'landmarks_origin': Tensor(shape=(68, 2), dtype=tf.float32),
    'pose_params': Tensor(shape=(7,), dtype=tf.float32),
    'roi': Tensor(shape=(4,), dtype=tf.float32),
    'shape_params': Tensor(shape=(199,), dtype=tf.float32),
    'tex_params': Tensor(shape=(199,), dtype=tf.float32),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
color_params Tensor (7,) tf.float32
exp_params Tensor (29,) tf.float32
illum_params Tensor (10,) tf.float32
image Image (450, 450, 3) tf.uint8
landmarks_2d Tensor (68, 2) tf.float32
landmarks_3d Tensor (68, 2) tf.float32
landmarks_origin Tensor (68, 2) tf.float32
pose_params Tensor (7,) tf.float32
roi Tensor (4,) tf.float32
shape_params Tensor (199,) tf.float32
tex_params Tensor (199,) tf.float32

Visualization

  • Citation:
@article{DBLP:journals/corr/ZhuLLSL15,
  author    = {Xiangyu Zhu and
               Zhen Lei and
               Xiaoming Liu and
               Hailin Shi and
               Stan Z. Li},
  title     = {Face Alignment Across Large Poses: {A} 3D Solution},
  journal   = {CoRR},
  volume    = {abs/1511.07212},
  year      = {2015},
  url       = {http://arxiv.org/abs/1511.07212},
  archivePrefix = {arXiv},
  eprint    = {1511.07212},
  timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/ZhuLLSL15},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}