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bob
bob.pad.face
Commits
beeeb418
Commit
beeeb418
authored
Dec 04, 2020
by
Amir MOHAMMADI
Browse files
delete left over config file
parent
fe2730d5
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bob/pad/face/config/qm_one_class_gmm.py
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fe2730d5
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
This file contains configurations to run Image Quality Measures (IQM) and one-class GMM based face PAD algorithm.
The settings of the preprocessor and extractor are tuned for the Replay-attack database.
The IQM features used in this algorithm/resource are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
"""
#=======================================================================================
sub_directory
=
'qm_one_class_gmm'
"""
Sub-directory where results will be placed.
You may change this setting using the ``--sub-directory`` command-line option
or the attribute ``sub_directory`` in a configuration file loaded **after**
this resource.
"""
#=======================================================================================
# define preprocessor:
from
..preprocessor
import
FaceCropAlign
from
bob.bio.video.preprocessor
import
Wrapper
from
bob.bio.video.utils
import
FrameSelector
FACE_SIZE
=
64
# The size of the resulting face
RGB_OUTPUT_FLAG
=
True
# RGB output
USE_FACE_ALIGNMENT
=
False
# use annotations
MAX_IMAGE_SIZE
=
None
# no limiting here
FACE_DETECTION_METHOD
=
None
# use annotations
MIN_FACE_SIZE
=
50
# skip small faces
_image_preprocessor
=
FaceCropAlign
(
face_size
=
FACE_SIZE
,
rgb_output_flag
=
RGB_OUTPUT_FLAG
,
use_face_alignment
=
USE_FACE_ALIGNMENT
,
max_image_size
=
MAX_IMAGE_SIZE
,
face_detection_method
=
FACE_DETECTION_METHOD
,
min_face_size
=
MIN_FACE_SIZE
)
_frame_selector
=
FrameSelector
(
selection_style
=
"all"
)
preprocessor
=
Wrapper
(
preprocessor
=
_image_preprocessor
,
frame_selector
=
_frame_selector
)
"""
In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces of the size
below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``. The preprocessed frame is the RGB
facial image, which is defined by ``RGB_OUTPUT_FLAG = True``.
"""
#=======================================================================================
# define extractor:
from
..extractor
import
ImageQualityMeasure
from
bob.bio.video.extractor
import
Wrapper
GALBALLY
=
True
MSU
=
True
DTYPE
=
None
extractor
=
Wrapper
(
ImageQualityMeasure
(
galbally
=
GALBALLY
,
msu
=
MSU
,
dtype
=
DTYPE
))
"""
In the feature extraction stage the Image Quality Measures are extracted from each frame of the preprocessed RGB video.
The features to be computed are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
"""
#=======================================================================================
# define algorithm:
from
bob.pad.base.algorithm
import
OneClassGMM
N_COMPONENTS
=
50
RANDOM_STATE
=
3
FRAME_LEVEL_SCORES_FLAG
=
True
algorithm
=
OneClassGMM
(
n_components
=
N_COMPONENTS
,
random_state
=
RANDOM_STATE
,
frame_level_scores_flag
=
FRAME_LEVEL_SCORES_FLAG
)
"""
The GMM with 50 clusters is trained using samples from the real class only. The pre-trained
GMM is next used to classify the data into *real* and *attack* classes.
One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
"""
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