26,413 research outputs found
Face Aging Effect Simulation using Hidden Factor Analysis Joint Sparse Representation
Face aging simulation has received rising investigations nowadays, whereas it
still remains a challenge to generate convincing and natural age-progressed
face images. In this paper, we present a novel approach to such an issue by
using hidden factor analysis joint sparse representation. In contrast to the
majority of tasks in the literature that handle the facial texture integrally,
the proposed aging approach separately models the person-specific facial
properties that tend to be stable in a relatively long period and the
age-specific clues that change gradually over time. It then merely transforms
the age component to a target age group via sparse reconstruction, yielding
aging effects, which is finally combined with the identity component to achieve
the aged face. Experiments are carried out on three aging databases, and the
results achieved clearly demonstrate the effectiveness and robustness of the
proposed method in rendering a face with aging effects. Additionally, a series
of evaluations prove its validity with respect to identity preservation and
aging effect generation
Dictionary Learning and Sparse Coding for Third-order Super-symmetric Tensors
Super-symmetric tensors - a higher-order extension of scatter matrices - are
becoming increasingly popular in machine learning and computer vision for
modelling data statistics, co-occurrences, or even as visual descriptors.
However, the size of these tensors are exponential in the data dimensionality,
which is a significant concern. In this paper, we study third-order
super-symmetric tensor descriptors in the context of dictionary learning and
sparse coding. Our goal is to approximate these tensors as sparse conic
combinations of atoms from a learned dictionary, where each atom is a symmetric
positive semi-definite matrix. Apart from the significant benefits to tensor
compression that this framework provides, our experiments demonstrate that the
sparse coefficients produced by the scheme lead to better aggregation of
high-dimensional data, and showcases superior performance on two common
computer vision tasks compared to the state-of-the-art.Comment: 13 pages, NIP
Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling
The "interpretation through synthesis" approach to analyze face images,
particularly Active Appearance Models (AAMs) method, has become one of the most
successful face modeling approaches over the last two decades. AAM models have
ability to represent face images through synthesis using a controllable
parameterized Principal Component Analysis (PCA) model. However, the accuracy
and robustness of the synthesized faces of AAM are highly depended on the
training sets and inherently on the generalizability of PCA subspaces. This
paper presents a novel Deep Appearance Models (DAMs) approach, an efficient
replacement for AAMs, to accurately capture both shape and texture of face
images under large variations. In this approach, three crucial components
represented in hierarchical layers are modeled using the Deep Boltzmann
Machines (DBM) to robustly capture the variations of facial shapes and
appearances. DAMs are therefore superior to AAMs in inferencing a
representation for new face images under various challenging conditions. The
proposed approach is evaluated in various applications to demonstrate its
robustness and capabilities, i.e. facial super-resolution reconstruction,
facial off-angle reconstruction or face frontalization, facial occlusion
removal and age estimation using challenging face databases, i.e. Labeled Face
Parts in the Wild (LFPW), Helen and FG-NET. Comparing to AAMs and other deep
learning based approaches, the proposed DAMs achieve competitive results in
those applications, thus this showed their advantages in handling occlusions,
facial representation, and reconstruction
Multi-modal Fusion for Diabetes Mellitus and Impaired Glucose Regulation Detection
Effective and accurate diagnosis of Diabetes Mellitus (DM), as well as its
early stage Impaired Glucose Regulation (IGR), has attracted much attention
recently. Traditional Chinese Medicine (TCM) [3], [5] etc. has proved that
tongue, face and sublingual diagnosis as a noninvasive method is a reasonable
way for disease detection. However, most previous works only focus on a single
modality (tongue, face or sublingual) for diagnosis, although different
modalities may provide complementary information for the diagnosis of DM and
IGR. In this paper, we propose a novel multi-modal classification method to
discriminate between DM (or IGR) and healthy controls. Specially, the tongue,
facial and sublingual images are first collected by using a non-invasive
capture device. The color, texture and geometry features of these three types
of images are then extracted, respectively. Finally, our so-called multi-modal
similar and specific learning (MMSSL) approach is proposed to combine features
of tongue, face and sublingual, which not only exploits the correlation but
also extracts individual components among them. Experimental results on a
dataset consisting of 192 Healthy, 198 DM and 114 IGR samples (all samples were
obtained from Guangdong Provincial Hospital of Traditional Chinese Medicine)
substantiate the effectiveness and superiority of our proposed method for the
diagnosis of DM and IGR, compared to the case of using a single modality.Comment: 9 pages, 8 figures, 30 conferenc
Fractional Local Neighborhood Intensity Pattern for Image Retrieval using Genetic Algorithm
In this paper, a new texture descriptor named "Fractional Local Neighborhood
Intensity Pattern" (FLNIP) has been proposed for content based image retrieval
(CBIR). It is an extension of the Local Neighborhood Intensity Pattern
(LNIP)[1]. FLNIP calculates the relative intensity difference between a
particular pixel and the center pixel of a 3x3 window by considering the
relationship with adjacent neighbors. In this work, the fractional change in
the local neighborhood involving the adjacent neighbors has been calculated
first with respect to one of the eight neighbors of the center pixel of a 3x3
window. Next, the fractional change has been calculated with respect to the
center itself. The two values of fractional change are next compared to
generate a binary bit pattern. Both sign and magnitude information are encoded
in a single descriptor as it deals with the relative change in magnitude in the
adjacent neighborhood i.e., the comparison of the fractional change. The
descriptor is applied on four multi-resolution images -- one being the raw
image and the other three being filtered gaussian images obtained by applying
gaussian filters of different standard deviations on the raw image to signify
the importance of exploring texture information at different resolutions in an
image. The four sets of distances obtained between the query and the target
image are then combined with a genetic algorithm based approach to improve the
retrieval performance by minimizing the distance between similar class images.
The performance of the method has been tested for image retrieval on four
popular databases. The precision and recall values observed on these databases
have been compared with recent state-of-art local patterns. The proposed method
has shown a significant improvement over many other existing methods.Comment: MTAP, Springer(Minor Revision
Face Recognition: A Novel Multi-Level Taxonomy based Survey
In a world where security issues have been gaining growing importance, face
recognition systems have attracted increasing attention in multiple application
areas, ranging from forensics and surveillance to commerce and entertainment.
To help understanding the landscape and abstraction levels relevant for face
recognition systems, face recognition taxonomies allow a deeper dissection and
comparison of the existing solutions. This paper proposes a new, more
encompassing and richer multi-level face recognition taxonomy, facilitating the
organization and categorization of available and emerging face recognition
solutions; this taxonomy may also guide researchers in the development of more
efficient face recognition solutions. The proposed multi-level taxonomy
considers levels related to the face structure, feature support and feature
extraction approach. Following the proposed taxonomy, a comprehensive survey of
representative face recognition solutions is presented. The paper concludes
with a discussion on current algorithmic and application related challenges
which may define future research directions for face recognition.Comment: This paper is a preprint of a paper submitted to IET Biometrics. If
accepted, the copy of record will be available at the IET Digital Librar
Densely tracking sequences of 3D face scans
3D face dense tracking aims to find dense inter-frame correspondences in a
sequence of 3D face scans and constitutes a powerful tool for many face
analysis tasks, e.g., 3D dynamic facial expression analysis. The majority of
the existing methods just fit a 3D face surface or model to a 3D target surface
without considering temporal information between frames. In this paper, we
propose a novel method for densely tracking sequences of 3D face scans, which
ex- tends the non-rigid ICP algorithm by adding a novel specific criterion for
temporal information. A novel fitting framework is presented for automatically
tracking a full sequence of 3D face scans. The results of experiments carried
out on the BU4D-FE database are promising, showing that the proposed algorithm
outperforms state-of-the-art algorithms for 3D face dense tracking.Comment: 8 page
Bayesian Fusion for Infrared and Visible Images
Infrared and visible image fusion has been a hot issue in image fusion. In
this task, a fused image containing both the gradient and detailed texture
information of visible images as well as the thermal radiation and highlighting
targets of infrared images is expected to be obtained. In this paper, a novel
Bayesian fusion model is established for infrared and visible images. In our
model, the image fusion task is cast into a regression problem. To measure the
variable uncertainty, we formulate the model in a hierarchical Bayesian manner.
Aiming at making the fused image satisfy human visual system, the model
incorporates the total-variation(TV) penalty. Subsequently, the model is
efficiently inferred by the expectation-maximization(EM) algorithm. We test our
algorithm on TNO and NIR image fusion datasets with several state-of-the-art
approaches. Compared with the previous methods, the novel model can generate
better fused images with high-light targets and rich texture details, which can
improve the reliability of the target automatic detection and recognition
system
Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder
Soft biometric modalities have shown their utility in different applications
including reducing the search space significantly. This leads to improved
recognition performance, reduced computation time, and faster processing of
test samples. Some common soft biometric modalities are ethnicity, gender, age,
hair color, iris color, presence of facial hair or moles, and markers. This
research focuses on performing ethnicity and gender classification on iris
images. We present a novel supervised autoencoder based approach, Deep
Class-Encoder, which uses class labels to learn discriminative representation
for the given sample by mapping the learned feature vector to its label. The
proposed model is evaluated on two datasets each for ethnicity and gender
classification. The results obtained using the proposed Deep Class-Encoder
demonstrate its effectiveness in comparison to existing approaches and
state-of-the-art methods.Comment: International Joint Conference on Biometrics, 201
A Sparse Representation of Complete Local Binary Pattern Histogram for Human Face Recognition
Human face recognition has been a long standing problem in computer vision
and pattern recognition. Facial analysis can be viewed as a two-fold problem,
namely (i) facial representation, and (ii) classification. So far, many face
representations have been proposed, a well-known method is the Local Binary
Pattern (LBP), which has witnessed a growing interest. In this respect, we
treat in this paper the issues of face representation as well as classification
in a novel manner. On the one hand, we use a variant to LBP, so-called Complete
Local Binary Pattern (CLBP), which differs from the basic LBP by coding a given
local region using a given central pixel and Sing_ Magnitude difference.
Subsequently, most of LBPbased descriptors use a fixed grid to code a given
facial image, which technique is, in most cases, not robust to pose variation
and misalignment. To cope with such issue, a representative Multi-Resolution
Histogram (MH) decomposition is adopted in our work. On the other hand, having
the histograms of the considered images extracted, we exploit their sparsity to
construct a so-called Sparse Representation Classifier (SRC) for further face
classification. Experimental results have been conducted on ORL face database,
and pointed out the superiority of our scheme over other popular
state-of-the-art techniques.Comment: Accepted (but unattended) in IEEE-EMBS International Conferences on
Biomedical and Health Informatics (BHI
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