Commit ed39e304 authored by Yu LINGHU's avatar Yu LINGHU Committed by Tiago de Freitas Pereira
Browse files

recommit all changes

parent 579aba47
......@@ -29,4 +29,4 @@ class BobIpTinyface(Base):
if annotations is not None:
return annotations[0]
else:
return None
return None
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......@@ -61,3 +61,5 @@ transformer = make_pipeline(
# Assemble the Vanilla Biometric pipeline and execute
pipeline = VanillaBiometricsPipeline(transformer, algorithm)
transformer = pipeline.transformer
import bob.bio.base
from bob.bio.face.preprocessor import FaceCrop
from bob.bio.base.transformers.preprocessor import PreprocessorTransformer
from bob.bio.face.extractor import opencv_model
from bob.bio.face.extractor import OpenCVModel
from bob.bio.base.extractor import Extractor
from bob.bio.base.transformers import ExtractorTransformer
from bob.bio.base.algorithm import Distance
......@@ -35,11 +35,11 @@ preprocessor_transformer = FaceCrop(
fixed_positions=fixed_positions,
)
cropped_positions = {"leye": (100, 140), "reye": (100, 95)}
cropped_positions = {"leye": (98, 144), "reye": (98, 76)}
# Preprocessor
preprocessor_transformer = FaceCrop(
cropped_image_size=(224, 224),
cropped_positions={"leye": (100, 140), "reye": (100, 95)},
cropped_positions={"leye": (98, 144), "reye": (98, 76)},
color_channel="rgb",
fixed_positions=fixed_positions,
)
......@@ -52,7 +52,7 @@ transform_extra_arguments = (
# Extractor
extractor_transformer = opencv_model()
extractor_transformer = OpenCVModel()
# Algorithm
......
......@@ -69,3 +69,4 @@ transformer = make_pipeline(
# Assemble the Vanilla Biometric pipeline and execute
pipeline = VanillaBiometricsPipeline(transformer, algorithm)
transformer = pipeline.transformer
......@@ -94,4 +94,4 @@ class MxNetModel(TransformerMixin, BaseEstimator):
return d
def _more_tags(self):
return {"stateless": True, "requires_fit": False}
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return {"stateless": True, "requires_fit": False}
......@@ -99,4 +99,4 @@ class OpenCVModel(TransformerMixin, BaseEstimator):
return d
def _more_tags(self):
return {"stateless": True, "requires_fit": False}
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return {"stateless": True, "requires_fit": False}
......@@ -165,4 +165,4 @@ class PyTorchLibraryModel(TransformerMixin, BaseEstimator):
return d
def _more_tags(self):
return {"stateless": True, "requires_fit": False}
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return {"stateless": True, "requires_fit": False}
......@@ -88,4 +88,4 @@ class TensorFlowModel(TransformerMixin, BaseEstimator):
return d
def _more_tags(self):
return {"stateless": True, "requires_fit": False}
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return {"stateless": True, "requires_fit": False}
......@@ -234,7 +234,7 @@ In this baseline, we use :py:class:`bob.bio.face.preprocessor.FaceCrop` with ``
and ``cropped_image_size=(160,160)``
as preprocessor, Inception Resnet v2 in [TFP18]_ as extractor, and ``distance-cosine`` as the algorithm. By testing on LFW database, we get the following ROC plot:
.. figure:: img/tensorflow_pipe.png
.. figure:: img/tensorflow_lfw_pipe.png
:figwidth: 75%
:align: center
:alt: Face recognition results of LFW database.
......
doc/img/opencv_lfw_pipe.png

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doc/img/opencv_lfw_pipe.png

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doc/img/opencv_lfw_pipe.png
doc/img/opencv_lfw_pipe.png
doc/img/opencv_lfw_pipe.png
doc/img/opencv_lfw_pipe.png
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......@@ -64,6 +64,7 @@ Image Feature Extractors
bob.bio.face.extractor.TensorFlowModel
bob.bio.face.extractor.OpenCVModel
Face Recognition Algorithms
~~~~~~~~~~~~~~~~~~~~~~~~~~~
......
......@@ -17,4 +17,4 @@ References
.. [ZSQ09] *W. Zhang, S. Shan, L. Qing, X. Chen and W. Gao*. **Are Gabor phases really useless for face recognition?** Pattern Analysis & Applications, 12:301-307, 2009.
.. [TFP18] de Freitas Pereira, Tiago, André Anjos, and Sébastien Marcel. "Heterogeneous face recognition using domain specific units." IEEE Transactions on Information Forensics and Security 14.7 (2018): 1803-1816.
.. [HRM06] *G. Heusch, Y. Rodriguez, and S. Marcel*. **Local Binary Patterns as an Image Preprocessing for Face Authentication**. In IEEE International Conference on Automatic Face and Gesture Recognition (AFGR), 2006.
.. [LGB18] *C. Li, M. Gunther and T. E. Boult*. **ECLIPSE: Ensembles of Centroids Leveraging Iteratively Processed Spatial Eclipse Clustering**. IEEE Winter Conference on Applications of Computer Vision (WACV), 2018.
.. [LGB18] *C. Li, M. Gunther and T. E. Boult*. **ECLIPSE: Ensembles of Centroids Leveraging Iteratively Processed Spatial Eclipse Clustering**. IEEE Winter Conference on Applications of Computer Vision (WACV), 2018.
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