20,140 research outputs found
Robust Face Recognition with Structural Binary Gradient Patterns
This paper presents a computationally efficient yet powerful binary framework
for robust facial representation based on image gradients. It is termed as
structural binary gradient patterns (SBGP). To discover underlying local
structures in the gradient domain, we compute image gradients from multiple
directions and simplify them into a set of binary strings. The SBGP is derived
from certain types of these binary strings that have meaningful local
structures and are capable of resembling fundamental textural information. They
detect micro orientational edges and possess strong orientation and locality
capabilities, thus enabling great discrimination. The SBGP also benefits from
the advantages of the gradient domain and exhibits profound robustness against
illumination variations. The binary strategy realized by pixel correlations in
a small neighborhood substantially simplifies the computational complexity and
achieves extremely efficient processing with only 0.0032s in Matlab for a
typical face image. Furthermore, the discrimination power of the SBGP can be
enhanced on a set of defined orientational image gradient magnitudes, further
enforcing locality and orientation. Results of extensive experiments on various
benchmark databases illustrate significant improvements of the SBGP based
representations over the existing state-of-the-art local descriptors in the
terms of discrimination, robustness and complexity. Codes for the SBGP methods
will be available at
http://www.eee.manchester.ac.uk/research/groups/sisp/software/
Automatic Facial Expression Recognition Using Features of Salient Facial Patches
Extraction of discriminative features from salient facial patches plays a
vital role in effective facial expression recognition. The accurate detection
of facial landmarks improves the localization of the salient patches on face
images. This paper proposes a novel framework for expression recognition by
using appearance features of selected facial patches. A few prominent facial
patches, depending on the position of facial landmarks, are extracted which are
active during emotion elicitation. These active patches are further processed
to obtain the salient patches which contain discriminative features for
classification of each pair of expressions, thereby selecting different facial
patches as salient for different pair of expression classes. One-against-one
classification method is adopted using these features. In addition, an
automated learning-free facial landmark detection technique has been proposed,
which achieves similar performances as that of other state-of-art landmark
detection methods, yet requires significantly less execution time. The proposed
method is found to perform well consistently in different resolutions, hence,
providing a solution for expression recognition in low resolution images.
Experiments on CK+ and JAFFE facial expression databases show the effectiveness
of the proposed system
LDOP: Local Directional Order Pattern for Robust Face Retrieval
The local descriptors have gained wide range of attention due to their
enhanced discriminative abilities. It has been proved that the consideration of
multi-scale local neighborhood improves the performance of the descriptor,
though at the cost of increased dimension. This paper proposes a novel method
to construct a local descriptor using multi-scale neighborhood by finding the
local directional order among the intensity values at different scales in a
particular direction. Local directional order is the multi-radius relationship
factor in a particular direction. The proposed local directional order pattern
(LDOP) for a particular pixel is computed by finding the relationship between
the center pixel and local directional order indexes. It is required to
transform the center value into the range of neighboring orders. Finally, the
histogram of LDOP is computed over whole image to construct the descriptor. In
contrast to the state-of-the-art descriptors, the dimension of the proposed
descriptor does not depend upon the number of neighbors involved to compute the
order; it only depends upon the number of directions. The introduced descriptor
is evaluated over the image retrieval framework and compared with the
state-of-the-art descriptors over challenging face databases such as PaSC, LFW,
PubFig, FERET, AR, AT&T, and ExtendedYale. The experimental results confirm the
superiority and robustness of the LDOP descriptor.Comment: Published in Multimedia Tools and Applications, Springe
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
Facial Expression Detection using Patch-based Eigen-face Isomap Networks
Automated facial expression detection problem pose two primary challenges
that include variations in expression and facial occlusions (glasses, beard,
mustache or face covers). In this paper we introduce a novel automated patch
creation technique that masks a particular region of interest in the face,
followed by Eigen-value decomposition of the patched faces and generation of
Isomaps to detect underlying clustering patterns among faces. The proposed
masked Eigen-face based Isomap clustering technique achieves 75% sensitivity
and 66-73% accuracy in classification of faces with occlusions and smiling
faces in around 1 second per image. Also, betweenness centrality, Eigen
centrality and maximum information flow can be used as network-based measures
to identify the most significant training faces for expression classification
tasks. The proposed method can be used in combination with feature-based
expression classification methods in large data sets for improving expression
classification accuracies.Comment: 6 pages,7 figures, IJCAI-HINA 201
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
Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval
In this paper, a new texture descriptor based on the local neighborhood
intensity difference is proposed for content based image retrieval (CBIR). For
computation of texture features like Local Binary Pattern (LBP), the center
pixel in a 3*3 window of an image is compared with all the remaining neighbors,
one pixel at a time to generate a binary bit pattern. It ignores the effect of
the adjacent neighbors of a particular pixel for its binary encoding and also
for texture description. The proposed method is based on the concept that
neighbors of a particular pixel hold a significant amount of texture
information that can be considered for efficient texture representation for
CBIR. Taking this into account, we develop a new texture descriptor, named as
Local Neighborhood Intensity Pattern (LNIP) which considers the relative
intensity difference between a particular pixel and the center pixel by
considering its adjacent neighbors and generate a sign and a magnitude pattern.
Since sign and magnitude patterns hold complementary information to each other,
these two patterns are concatenated into a single feature descriptor to
generate a more concrete and useful feature descriptor. The proposed descriptor
has been tested for image retrieval on four databases, including three texture
image databases - Brodatz texture image database, MIT VisTex database and
Salzburg texture database and one face database AT&T face database. The
precision and recall values observed on these databases are compared with some
state-of-art local patterns. The proposed method showed a significant
improvement over many other existing methods.Comment: Expert Systems with Applications(Elsevier
Face Retrieval using Frequency Decoded Local Descriptor
The local descriptors have been the backbone of most of the computer vision
problems. Most of the existing local descriptors are generated over the raw
input images. In order to increase the discriminative power of the local
descriptors, some researchers converted the raw image into multiple images with
the help of some high and low pass frequency filters, then the local
descriptors are computed over each filtered image and finally concatenated into
a single descriptor. By doing so, these approaches do not utilize the inter
frequency relationship which causes the less improvement in the discriminative
power of the descriptor that could be achieved. In this paper, this problem is
solved by utilizing the decoder concept of multi-channel decoded local binary
pattern over the multi-frequency patterns. A frequency decoded local binary
pattern (FDLBP) is proposed with two decoders. Each decoder works with one low
frequency pattern and two high frequency patterns. Finally, the descriptors
from both decoders are concatenated to form the single descriptor. The face
retrieval experiments are conducted over four benchmarks and challenging
databases such as PaSC, LFW, PubFig, and ESSEX. The experimental results
confirm the superiority of the FDLBP descriptor as compared to the
state-of-the-art descriptors such as LBP, SOBEL_LBP, BoF_LBP, SVD_S_LBP, mdLBP,
etc.Comment: Accepted in Multimedia Tools and Applications, Springe
Gender Classification Using Gradient Direction Pattern
A novel methodology for gender classification is presented in this paper. It
extracts feature from local region of a face using gray color intensity
difference. The facial area is divided into sub-regions and GDP histogram
extracted from those regions are concatenated into a single vector to represent
the face. The classification accuracy obtained by using support vector machine
has outperformed all traditional feature descriptors for gender classification.
It is evaluated on the images collected from FERET database and obtained very
high accuracy.Comment: 3 pages, 5 figures, 3 tables, SCI journa
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications
Facial expressions are an important way through which humans interact
socially. Building a system capable of automatically recognizing facial
expressions from images and video has been an intense field of study in recent
years. Interpreting such expressions remains challenging and much research is
needed about the way they relate to human affect. This paper presents a general
overview of automatic RGB, 3D, thermal and multimodal facial expression
analysis. We define a new taxonomy for the field, encompassing all steps from
face detection to facial expression recognition, and describe and classify the
state of the art methods accordingly. We also present the important datasets
and the bench-marking of most influential methods. We conclude with a general
discussion about trends, important questions and future lines of research
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