282,869 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
3D face recognition using multiview keypoint matching
A novel algorithm for 3D face recognition based point cloud rotations, multiple projections, and voted keypoint matching is proposed and evaluated. The basic idea is to rotate each 3D point cloud representing an individual’s face around the x, y or z axes, iteratively projecting the 3D points onto multiple 2.5D images at each step of the rotation. Labelled keypoints are then extracted from the resulting collection of 2.5D images, and this much smaller set of keypoints replaces the original face scan and its projections in the face database. Unknown test faces are recognised firstly by performing the same multiview keypoint extraction technique, and secondly, the application of a new weighted keypoint matching algorithm. In an extensive evaluation using the GavabDB 3D face recognition dataset (61 subjects, 9 scans per subject), our method achieves up to 95% recognition accuracy for faces with neutral expressions only, and over 90% accuracy for face recognition where expressions (such as a smile or a strong laugh) and random faceoccluding gestures are permitted
Real-time 3D Face Recognition using Line Projection and Mesh Sampling
The main contribution of this paper is to present a novel method for automatic 3D face recognition based on sampling a 3D mesh structure in the presence of noise. A structured light method using line projection is employed where a 3D face is reconstructed from a single 2D shot. The process from image acquisition to recognition is described with focus on its real-time operation. Recognition results are presented and it is demonstrated that it can perform recognition in just over one second per subject in continuous operation mode and thus, suitable for real time operation
Fuzzy Interval-Valued Multi Criteria Based Decision Making for Ranking Features in Multi-Modal 3D Face Recognition
Soodamani Ramalingam, 'Fuzzy interval-valued multi criteria based decision making for ranking features in multi-modal 3D face recognition', Fuzzy Sets and Systems, In Press version available online 13 June 2017. This is an Open Access paper, made available under the Creative Commons license CC BY 4.0 https://creativecommons.org/licenses/by/4.0/This paper describes an application of multi-criteria decision making (MCDM) for multi-modal fusion of features in a 3D face recognition system. A decision making process is outlined that is based on the performance of multi-modal features in a face recognition task involving a set of 3D face databases. In particular, the fuzzy interval valued MCDM technique called TOPSIS is applied for ranking and deciding on the best choice of multi-modal features at the decision stage. It provides a formal mechanism of benchmarking their performances against a set of criteria. The technique demonstrates its ability in scaling up the multi-modal features.Peer reviewedProo
Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition
Two approaches are proposed for cross-pose face recognition, one is based on
the 3D reconstruction of facial components and the other is based on the deep
Convolutional Neural Network (CNN). Unlike most 3D approaches that consider
holistic faces, the proposed approach considers 3D facial components. It
segments a 2D gallery face into components, reconstructs the 3D surface for
each component, and recognizes a probe face by component features. The
segmentation is based on the landmarks located by a hierarchical algorithm that
combines the Faster R-CNN for face detection and the Reduced Tree Structured
Model for landmark localization. The core part of the CNN-based approach is a
revised VGG network. We study the performances with different settings on the
training set, including the synthesized data from 3D reconstruction, the
real-life data from an in-the-wild database, and both types of data combined.
We investigate the performances of the network when it is employed as a
classifier or designed as a feature extractor. The two recognition approaches
and the fast landmark localization are evaluated in extensive experiments, and
compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table
3D Face Recognition using Significant Point based SULD Descriptor
In this work, we present a new 3D face recognition method based on Speeded-Up
Local Descriptor (SULD) of significant points extracted from the range images
of faces. The proposed model consists of a method for extracting distinctive
invariant features from range images of faces that can be used to perform
reliable matching between different poses of range images of faces. For a given
3D face scan, range images are computed and the potential interest points are
identified by searching at all scales. Based on the stability of the interest
point, significant points are extracted. For each significant point we compute
the SULD descriptor which consists of vector made of values from the convolved
Haar wavelet responses located on concentric circles centred on the significant
point, and where the amount of Gaussian smoothing is proportional to the radii
of the circles. Experimental results show that the newly proposed method
provides higher recognition rate compared to other existing contemporary models
developed for 3D face recognition
3D Face Recognition: Feature Extraction Based on Directional Signatures from Range Data and Disparity Maps
In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation
A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment
Face analysis techniques have become a crucial component of human-machine
interaction in the fields of assistive and humanoid robotics. However, the
variations in head-pose that arise naturally in these environments are still a
great challenge. In this paper, we present a real-time capable 3D face
modelling framework for 2D in-the-wild images that is applicable for robotics.
The fitting of the 3D Morphable Model is based exclusively on automatically
detected landmarks. After fitting, the face can be corrected in pose and
transformed back to a frontal 2D representation that is more suitable for face
recognition. We conduct face recognition experiments with non-frontal images
from the MUCT database and uncontrolled, in the wild images from the PaSC
database, the most challenging face recognition database to date, showing an
improved performance. Finally, we present our SCITOS G5 robot system, which
incorporates our framework as a means of image pre-processing for face
analysis
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