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Improving the Accuracy of Face Detection for Damaged Video and
Distant Targets
Jun-Horng Chen
Department of Communication Engineering, Oriental Institute of Technology, New Taiepi City, Taiwan
Keywords:
Error Concealment, Face Detection, Super-resolution.
Abstract:
This work aims at improving the accuracy of face detection in two scenarios, when the video quality is de-
teriorated by the transmission link and when the target is far away from the camera. In block based coding,
the packet loss inevitably makes the corrupted face image lacks some blocks. This work proposes the sparse
modeling error concealment can coarsely recover the lost blocks, the fine texture can be obtained by dimin-
ishing the edge discontinuity, and a satisfied result for face detection can thus be recovered. Furthermore, this
work utilizes the relationship learning super-resolution method to enhance the resolution in the case of face
image taken from a long distance. Experimental results demonstrate that the proposed approach can effectively
increase the accuracy of face detection for severely degraded and low resolution face images.
1 INTRODUCTION
As the continuous growth of ubiquitously installed
cameras, the applications of computer vision tech-
niques are rapidly developed. Over the past decades,
face recognition has become one of the most popu-
lar biometric applications. The widespread surveil-
lance systems encourage the development and estab-
lishment of face recognition in public area. Gener-
ally, face recognition systems are composed of two
stages: detection stage and recognition stage, and are
analyzed separately (Marciniak et al., 2013). That is,
if the face can not be detected at the first stage, system
with high accuracy of recognition will not function
expectedly.
However, in some surveillance systems, the video
signal is fed into the recognition system via trans-
mission link. Therefore, the image quality is in-
evitably degraded by imperfect transmission, and the
degraded face video definitely diminishes the accu-
racy of recognition. Generally in video communica-
tion, the error concealment technique which recovers
the corrupted Macroblocks(MB) at the decoder site is
proposed for maintenance of the visual quality. The
sparse modeling error concealment (Lakshman et al.,
2010) has been proven to be an effective way to en-
hance the visual quality. Accordingly, this work will
utilize sparse modeling error concealment to recover
the corrupted face images so that the face detection
accuracy can thus be improved.
Furthermore, the impressive performance of face
recognition system is usually measured in controlled
conditions, such as ambient illumination, pose, res-
olution, etc. For example, in FRVT 2006 (Phillips
et al., 2007) , the interpupillary distance (IPD) of
some experiments can be as high as 400 pixels. It
is the main reason for some deployments (Bonner,
2001)(Dempsey and Forst, 2010) did not meet the
required accuracy. As for some successful deploy-
ments, the subject’s cooperation and the controlled
conditions are required and expected. Since the super-
resolution (SR) process is proposed to enhance reso-
lution image from one or multiple low resolution im-
ages, this work will utilize an effective SR approach
to estimate a high resolution image from a very low
resolution image which is taken by a camera located
at a long distance away from target.
2 SPARSE MODELING ERROR
CONCEALMENT
The sparse modeling error concealment technique
which recovers the corrupted or lost blocks at the
decoder site is proposed for maintenance of the im-
age visual quality in imperfect transmission link. In
contrast to the traditional error resilience techniques
e.g. FEC and ARQ, the error concealment is ex-
pected to diminish the channeleffect without the over-
351
Chen J..
Improving the Accuracy of Face Detection for Damaged Video and Distant Targets.
DOI: 10.5220/0005161603510355
In Proceedings of the International Conference on Neural Computation Theory and Applications (NCTA-2014), pages 351-355
ISBN: 978-989-758-054-3
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)