1,909 research outputs found
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second
authorshi
Deep Feature-based Face Detection on Mobile Devices
We propose a deep feature-based face detector for mobile devices to detect
user's face acquired by the front facing camera. The proposed method is able to
detect faces in images containing extreme pose and illumination variations as
well as partial faces. The main challenge in developing deep feature-based
algorithms for mobile devices is the constrained nature of the mobile platform
and the non-availability of CUDA enabled GPUs on such devices. Our
implementation takes into account the special nature of the images captured by
the front-facing camera of mobile devices and exploits the GPUs present in
mobile devices without CUDA-based frameorks, to meet these challenges.Comment: ISBA 201
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