User Guide¶

You can used this Bob package to extract image-quality features for face-PAD applications. Two sets of quality-features are implemented in this package:

The image-quality measures proposed by Galbally et al. (IEEE TIP 2014), and
The image-quality features proposed by Wen et al. (IEEE TIFS 2015).

The package includes separate modules for implementing the two feature-sets. The module galbally_iqm_features implements the features proposed by Gabally et al., and the module msu_iqa_features implements the features proposed by Wen et al. In each module, a single function needs to be called, to retrieve all the features implemented in the module. The examples below show how to use the functions in the two modules.

Note that both feature-sets are extracted from still-images. However, in face-PAD experiments, we typically process videos. Therefore, the examples below use a video as input, but show how to extract image-quality features for a single frame.

Note also, that in the examples below, the input to the feature-extraction functions are full-frames. If you wish to extract features only for the face-region, you will have to first construct an image containing only the region of interest, and pass that as the parameter to the feature-extraction functions.

Computing Galbally’s image-quality measures¶

The function compute_quality_features() (in the module galbally_iqm_features) can be used to compute 18 image-quality measures proposed by Galbally et al. Note that Galbally et al. proposed 25 features in their paper. This package implements the following 18 features from their paper, namely: [mse, psnr, ad, sc, nk, md, lmse, nae, snrv, ramdv, mas, mams, sme, gme, gpe, ssim, vif, hlfi]. The function galbally_iqm_features::compute_quality_features() returns a 1D numpy array of length 18, containing the feature-values in the order listed above.

>>> from bob.ip.qualitymeasure import galbally_iqm_features as iqm
>>> video4d = bob.io.video.reader(video_file) 
>>> rgb_frame = video4d[0]
>>> print(rgb_frame.shape)
(3, 480, 720)
>>> gf_set = iqm.compute_quality_features(rgb_frame)
>>> print(len(gf_set))
18

In the example-code above, we have used a color (RGB) image as input to the function compute_quality_features(). In fact, the features proposed by Galbally et al. are computed over gray-level images. Therefore, the function galbally_iqm_features::compute_quality_features() takes as input either a RGB color-image, or a gray-level image. (The input image should be a numpy-array. RGB color-images should be in the format expected by Bob.) When the input image is 3-dimensional, the first dimension being ‘3’ (as is the case in the example above), the input is assumed to represent a color RGB image, and is first converted to a gray-level image. If the input is 2-dimensional (say, a numpy array of shape [480, 720]), then it is assumed to represent a gray-level image, and the RGB-to-gray conversion step is skipped.

Computing Wen’s (MSU) image-quality measures¶

The code below shows how to compute the image-quality features proposed by Wen et al. (Here, we refer to these features as ‘MSU features’.) These features are computed from a RGB color-image. The 3 feature-types (specularity, image-blur, color-diversity) all together form a 121-D feature-vector. The function compute_msu_iqa_features() (from the module msu_iqa_features) returns a 1D numpy array of length 121.

>>> from bob.ip.qualitymeasure import msu_iqa_features as iqa
>>> video4d = bob.io.video.reader(video_file) 
>>> rgb_frame = video4d[0]
>>> msuf_set = iqa.compute_msu_iqa_features(rgb_frame)
>>> print(len(msuf_set))
121