3,932 research outputs found
Face Recognition from Sequential Sparse 3D Data via Deep Registration
Previous works have shown that face recognition with high accurate 3D data is
more reliable and insensitive to pose and illumination variations. Recently,
low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and
DoE based structured light systems enable us to access 3D data easily, e.g.,
via a mobile phone. However, such devices only provide sparse(limited speckles
in structured light system) and noisy 3D data which can not support face
recognition directly. In this paper, we aim at achieving high-performance face
recognition for devices equipped with such modules which is very meaningful in
practice as such devices will be very popular. We propose a framework to
perform face recognition by fusing a sequence of low-quality 3D data. As 3D
data are sparse and noisy which can not be well handled by conventional methods
like the ICP algorithm, we design a PointNet-like Deep Registration
Network(DRNet) which works with ordered 3D point coordinates while preserving
the ability of mining local structures via convolution. Meanwhile we develop a
novel loss function to optimize our DRNet based on the quaternion expression
which obviously outperforms other widely used functions. For face recognition,
we design a deep convolutional network which takes the fused 3D depth-map as
input based on AMSoftmax model. Experiments show that our DRNet can achieve
rotation error 0.95{\deg} and translation error 0.28mm for registration. The
face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001
97.5% on Bosphorus dataset which is comparable with state-of-the-art
high-quality data based recognition performance.Comment: To be appeared in ICB201
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D
facial pose tracking and face model adaptation on-the-fly, in the unconstrained
scenarios with heavy occlusions and arbitrary facial expression variations.
Specifically, we introduce a statistical 3D morphable model that flexibly
describes the distribution of points on the surface of the face model, with an
efficient switchable online adaptation that gradually captures the identity of
the tracked subject and rapidly constructs a suitable face model when the
subject changes. Moreover, unlike prior art that employed ICP-based facial pose
estimation, to improve robustness to occlusions, we propose a ray visibility
constraint that regularizes the pose based on the face model's visibility with
respect to the input point cloud. Ablation studies and experimental results on
Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective
and outperforms completing state-of-the-art depth-based methods
3D facial merging for virtual human reconstruction
There is an increasing need of easy and affordable technologies to automatically generate virtual 3D models from their real counterparts. In particular, 3D human reconstruction has driven the creation of many clever techniques, most of them based on the visual hull (VH) concept. Such techniques do not require expensive hardware; however, they tend to yield 3D humanoids with realistic bodies but mediocre faces, since VH cannot handle concavities. On the other hand, structured light projectors allow to capture very accurate depth data, and thus to reconstruct realistic faces, but they are too expensive to use several of them. We have developed a technique to merge a VH-based 3D mesh of a reconstructed humanoid and the depth data of its face, captured by a single structured light projector. By combining the advantages of both systems in a simple setting, we are able to reconstruct realistic 3D human models with believable faces
3D video coding and transmission
The capture, transmission, and display of
3D content has gained a lot of attention in the last few
years. 3D multimedia content is no longer con fined to
cinema theatres but is being transmitted using stereoscopic
video over satellite, shared on Blu-RayTMdisks,
or sent over Internet technologies. Stereoscopic displays
are needed at the receiving end and the viewer needs to
wear special glasses to present the two versions of the
video to the human vision system that then generates
the 3D illusion. To be more e ffective and improve the
immersive experience, more views are acquired from a
larger number of cameras and presented on di fferent displays,
such as autostereoscopic and light field displays.
These multiple views, combined with depth data, also
allow enhanced user experiences and new forms of interaction
with the 3D content from virtual viewpoints.
This type of audiovisual information is represented by a
huge amount of data that needs to be compressed and
transmitted over bandwidth-limited channels. Part of
the COST Action IC1105 \3D Content Creation, Coding
and Transmission over Future Media Networks" (3DConTourNet)
focuses on this research challenge.peer-reviewe
AFFECT-PRESERVING VISUAL PRIVACY PROTECTION
The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this dissertation, we propose to balance the privacy protection and the utility of the data by preserving the privacy-insensitive information, such as pose and expression, which is useful in many applications involving visual understanding.
The Intellectual Merits of the dissertation include a novel framework for visual privacy protection by manipulating facial image and body shape of individuals, which: (1) is able to conceal the identity of individuals; (2) provide a way to preserve the utility of the data, such as expression and pose information; (3) balance the utility of the data and capacity of the privacy protection.
The Broader Impacts of the dissertation focus on the significance of privacy protection on visual data, and the inadequacy of current privacy enhancing technologies in preserving affect and behavioral attributes of the visual content, which are highly useful for behavior observation in educational and medical settings. This work in this dissertation represents one of the first attempts in achieving both goals simultaneously
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