13,854 research outputs found
Robust Facial Landmark Localization Based on Texture and Pose Correlated Initialization
Robust facial landmark localization remains a challenging task when faces are
partially occluded. Recently, the cascaded pose regression has attracted
increasing attentions, due to it's superior performance in facial landmark
localization and occlusion detection. However, such an approach is sensitive to
initialization, where an improper initialization can severly degrade the
performance. In this paper, we propose a Robust Initialization for Cascaded
Pose Regression (RICPR) by providing texture and pose correlated initial shapes
for the testing face. By examining the correlation of local binary patterns
histograms between the testing face and the training faces, the shapes of the
training faces that are most correlated with the testing face are selected as
the texture correlated initialization. To make the initialization more robust
to various poses, we estimate the rough pose of the testing face according to
five fiducial landmarks located by multitask cascaded convolutional networks.
Then the pose correlated initial shapes are constructed by the mean face's
shape and the rough testing face pose. Finally, the texture correlated and the
pose correlated initial shapes are joined together as the robust
initialization. We evaluate RICPR on the challenging dataset of COFW. The
experimental results demonstrate that the proposed scheme achieves better
performances than the state-of-the-art methods in facial landmark localization
and occlusion detection
When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition
Most of the face recognition works focus on specific modules or demonstrate a
research idea. This paper presents a pose-invariant 3D-aided 2D face
recognition system (UR2D) that is robust to pose variations as large as 90? by
leveraging deep learning technology. The architecture and the interface of UR2D
are described, and each module is introduced in detail. Extensive experiments
are conducted on the UHDB31 and IJB-A, demonstrating that UR2D outperforms
existing 2D face recognition systems such as VGG-Face, FaceNet, and a
commercial off-the-shelf software (COTS) by at least 9% on the UHDB31 dataset
and 3% on the IJB-A dataset on average in face identification tasks. UR2D also
achieves state-of-the-art performance of 85% on the IJB-A dataset by comparing
the Rank-1 accuracy score from template matching. It fills a gap by providing a
3D-aided 2D face recognition system that has compatible results with 2D face
recognition systems using deep learning techniques.Comment: Submitted to Special Issue on Biometrics in the Wild, Image and
Vision Computin
A Survey of the Trends in Facial and Expression Recognition Databases and Methods
Automated facial identification and facial expression recognition have been
topics of active research over the past few decades. Facial and expression
recognition find applications in human-computer interfaces, subject tracking,
real-time security surveillance systems and social networking. Several holistic
and geometric methods have been developed to identify faces and expressions
using public and local facial image databases. In this work we present the
evolution in facial image data sets and the methodologies for facial
identification and recognition of expressions such as anger, sadness,
happiness, disgust, fear and surprise. We observe that most of the earlier
methods for facial and expression recognition aimed at improving the
recognition rates for facial feature-based methods using static images.
However, the recent methodologies have shifted focus towards robust
implementation of facial/expression recognition from large image databases that
vary with space (gathered from the internet) and time (video recordings). The
evolution trends in databases and methodologies for facial and expression
recognition can be useful for assessing the next-generation topics that may
have applications in security systems or personal identification systems that
involve "Quantitative face" assessments.Comment: 16 pages, 4 figures, 3 tables, International Journal of Computer
Science and Engineering Survey, October, 201
CaricatureShop: Personalized and Photorealistic Caricature Sketching
In this paper, we propose the first sketching system for interactively
personalized and photorealistic face caricaturing. Input an image of a human
face, the users can create caricature photos by manipulating its facial feature
curves. Our system firstly performs exaggeration on the recovered 3D face model
according to the edited sketches, which is conducted by assigning the laplacian
of each vertex a scaling factor. To construct the mapping between 2D sketches
and a vertex-wise scaling field, a novel deep learning architecture is
developed. With the obtained 3D caricature model, two images are generated, one
obtained by applying 2D warping guided by the underlying 3D mesh deformation
and the other obtained by re-rendering the deformed 3D textured model. These
two images are then seamlessly integrated to produce our final output. Due to
the severely stretching of meshes, the rendered texture is of blurry
appearances. A deep learning approach is exploited to infer the missing details
for enhancing these blurry regions. Moreover, a relighting operation is
invented to further improve the photorealism of the result. Both quantitative
and qualitative experiment results validated the efficiency of our sketching
system and the superiority of our proposed techniques against existing methods.Comment: 12 pages,16 figures,submitted to IEEE TVC
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image
We propose a deep inverse rendering framework for indoor scenes. From a
single RGB image of an arbitrary indoor scene, we create a complete scene
reconstruction, estimating shape, spatially-varying lighting, and
spatially-varying, non-Lambertian surface reflectance. To train this network,
we augment the SUNCG indoor scene dataset with real-world materials and render
them with a fast, high-quality, physically-based GPU renderer to create a
large-scale, photorealistic indoor dataset. Our inverse rendering network
incorporates physical insights -- including a spatially-varying spherical
Gaussian lighting representation, a differentiable rendering layer to model
scene appearance, a cascade structure to iteratively refine the predictions and
a bilateral solver for refinement -- allowing us to jointly reason about shape,
lighting, and reflectance. Experiments show that our framework outperforms
previous methods for estimating individual scene components, which also enables
various novel applications for augmented reality, such as photorealistic object
insertion and material editing. Code and data will be made publicly available
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Radial feature descriptors for cell classification and recommendation
This paper introduces computational tools for cell classification into normal and abnormal, as well as content-based-image-retrieval (CBIR) for cell recommendation. It also proposes the radial feature descriptors (RFD), which define evenly interspaced segments around the nucleus, and proportional to the convexity of the nuclear boundary. Experiments consider Herlev and CRIC image databases as input to classification via Random Forest and bootstrap; we compare 14 different feature sets by means of False Negative Rate (FNR) and Kappa (κ), obtaining FNR =0.02 and κ=0.89 for Herlev, and FNR =0.14 and κ=0.78 for CRIC. Next, we sort and rank cell images using convolutional neural networks and evaluate performance with the Mean Average Precision (MAP), achieving MAP =0.84 and MAP =0.82 for Herlev and CRIC, respectively. Cell classification show encouraging results regarding RFD, including its sensitivity to intensity variation around the nuclear membrane as it bypasses cytoplasm segmentation
Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition
We further exploit the representational power of Haar wavelet and present a
novel low-level face representation named Shape Primitives Histogram (SPH) for
face recognition. Since human faces exist abundant shape features, we address
the face representation issue from the perspective of the shape feature
extraction. In our approach, we divide faces into a number of tiny shape
fragments and reduce these shape fragments to several uniform atomic shape
patterns called Shape Primitives. A convolution with Haar Wavelet templates is
applied to each shape fragment to identify its belonging shape primitive. After
that, we do a histogram statistic of shape primitives in each spatial local
image patch for incorporating the spatial information. Finally, each face is
represented as a feature vector via concatenating all the local histograms of
shape primitives. Four popular face databases, namely ORL, AR, Yale-B and LFW-a
databases, are employed to evaluate SPH and experimentally study the choices of
the parameters. Extensive experimental results demonstrate that the proposed
approach outperform the state-of-the-arts.Comment: second version, two columns and 11 page
A Pixel-Based Framework for Data-Driven Clothing
With the aim of creating virtual cloth deformations more similar to real
world clothing, we propose a new computational framework that recasts three
dimensional cloth deformation as an RGB image in a two dimensional pattern
space. Then a three dimensional animation of cloth is equivalent to a sequence
of two dimensional RGB images, which in turn are driven/choreographed via
animation parameters such as joint angles. This allows us to leverage popular
CNNs to learn cloth deformations in image space. The two dimensional cloth
pixels are extended into the real world via standard body skinning techniques,
after which the RGB values are interpreted as texture offsets and displacement
maps. Notably, we illustrate that our approach does not require accurate
unclothed body shapes or robust skinning techniques. Additionally, we discuss
how standard image based techniques such as image partitioning for higher
resolution, GANs for merging partitioned image regions back together, etc., can
readily be incorporated into our framework
Combining 3D Morphable Models: A Large scale Face-and-Head Model
Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for
representing the 3D surfaces of an object class. In this context, we identify
an interesting question that has previously not received research attention: is
it possible to combine two or more 3DMMs that (a) are built using different
templates that perhaps only partly overlap, (b) have different representation
capabilities and (c) are built from different datasets that may not be
publicly-available? In answering this question, we make two contributions.
First, we propose two methods for solving this problem: i. use a regressor to
complete missing parts of one model using the other, ii. use the Gaussian
Process framework to blend covariance matrices from multiple models. Second, as
an example application of our approach, we build a new face-and-head shape
model that combines the variability and facial detail of the LSFM with the full
head modelling of the LYHM. The resulting combined shape model achieves
state-of-the-art performance and outperforms existing head models by a large
margin. Finally, as an application experiment, we reconstruct full head
representations from single, unconstrained images by utilizing our proposed
large-scale model in conjunction with the FaceWarehouse blendshapes for
handling expressions.Comment: 9 pages, 8 figures. To appear in the Proceedings of Computer Vision
and Pattern Recognition (CVPR), June 2019, Los Angeles, US
Low-Level Features for Image Retrieval Based on Extraction of Directional Binary Patterns and Its Oriented Gradients Histogram
In this paper, we present a novel approach for image retrieval based on
extraction of low level features using techniques such as Directional Binary
Code, Haar Wavelet transform and Histogram of Oriented Gradients. The DBC
texture descriptor captures the spatial relationship between any pair of
neighbourhood pixels in a local region along a given direction, while Local
Binary Patterns descriptor considers the relationship between a given pixel and
its surrounding neighbours. Therefore, DBC captures more spatial information
than LBP and its variants, also it can extract more edge information than LBP.
Hence, we employ DBC technique in order to extract grey level texture feature
from each RGB channels individually and computed texture maps are further
combined which represents colour texture features of an image. Then, we
decomposed the extracted colour texture map and original image using Haar
wavelet transform. Finally, we encode the shape and local features of wavelet
transformed images using Histogram of Oriented Gradients for content based
image retrieval. The performance of proposed method is compared with existing
methods on two databases such as Wang's corel image and Caltech 256. The
evaluation results show that our approach outperforms the existing methods for
image retrieval.Comment: 7 Figures, 5 Tables 16 Pages in Computer Applications: An
International Journal (CAIJ), Vol.2, No.1, February 201
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