838 research outputs found
Motion deblurring of faces
Face analysis is a core part of computer vision, in which remarkable progress
has been observed in the past decades. Current methods achieve recognition and
tracking with invariance to fundamental modes of variation such as
illumination, 3D pose, expressions. Notwithstanding, a much less standing mode
of variation is motion deblurring, which however presents substantial
challenges in face analysis. Recent approaches either make oversimplifying
assumptions, e.g. in cases of joint optimization with other tasks, or fail to
preserve the highly structured shape/identity information. Therefore, we
propose a data-driven method that encourages identity preservation. The
proposed model includes two parallel streams (sub-networks): the first deblurs
the image, the second implicitly extracts and projects the identity of both the
sharp and the blurred image in similar subspaces. We devise a method for
creating realistic motion blur by averaging a variable number of frames to
train our model. The averaged images originate from a 2MF2 dataset with 10
million facial frames, which we introduce for the task. Considering deblurring
as an intermediate step, we utilize the deblurred outputs to conduct a thorough
experimentation on high-level face analysis tasks, i.e. landmark localization
and face verification. The experimental evaluation demonstrates the superiority
of our method
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
Advances in image restoration and enhancement techniques have led to
discussion about how such algorithmscan be applied as a pre-processing step to
improve automatic visual recognition. In principle, techniques like deblurring
and super-resolution should yield improvements by de-emphasizing noise and
increasing signal in an input image. But the historically divergent goals of
the computational photography and visual recognition communities have created a
significant need for more work in this direction. To facilitate new research,
we introduce a new benchmark dataset called UG^2, which contains three
difficult real-world scenarios: uncontrolled videos taken by UAVs and manned
gliders, as well as controlled videos taken on the ground. Over 160,000
annotated frames forhundreds of ImageNet classes are available, which are used
for baseline experiments that assess the impact of known and unknown image
artifacts and other conditions on common deep learning-based object
classification approaches. Further, current image restoration and enhancement
techniques are evaluated by determining whether or not theyimprove baseline
classification performance. Results showthat there is plenty of room for
algorithmic innovation, making this dataset a useful tool going forward.Comment: Supplemental material: https://goo.gl/vVM1xe, Dataset:
https://goo.gl/AjA6En, CVPR 2018 Prize Challenge: ug2challenge.or
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