2,389 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/
Leveraging the Power of Gabor Phase for Face Identification: A Block Matching Approach
Different from face verification, face identification is much more demanding.
To reach comparable performance, an identifier needs to be roughly N times
better than a verifier. To expect a breakthrough in face identification, we
need a fresh look at the fundamental building blocks of face recognition. In
this paper we focus on the selection of a suitable signal representation and
better matching strategy for face identification. We demonstrate how Gabor
phase could be leveraged to improve the performance of face identification by
using the Block Matching method. Compared to the existing approaches, the
proposed method features much lower algorithmic complexity: face images are
only filtered by a single-scale Gabor filter pair and the matching is performed
between any pairs of face images at hand without involving any training
process. Benchmark evaluations show that the proposed approach is totally
comparable to and even better than state-of-the-art algorithms, which are
typically based on more features extracted from a large set of Gabor faces
and/or rely on heavy training processes
Face Recognition: From Traditional to Deep Learning Methods
Starting in the seventies, face recognition has become one of the most
researched topics in computer vision and biometrics. Traditional methods based
on hand-crafted features and traditional machine learning techniques have
recently been superseded by deep neural networks trained with very large
datasets. In this paper we provide a comprehensive and up-to-date literature
review of popular face recognition methods including both traditional
(geometry-based, holistic, feature-based and hybrid methods) and deep learning
methods
Texture image analysis and texture classification methods - A review
Tactile texture refers to the tangible feel of a surface and visual texture
refers to see the shape or contents of the image. In the image processing, the
texture can be defined as a function of spatial variation of the brightness
intensity of the pixels. Texture is the main term used to define objects or
concepts of a given image. Texture analysis plays an important role in computer
vision cases such as object recognition, surface defect detection, pattern
recognition, medical image analysis, etc. Since now many approaches have been
proposed to describe texture images accurately. Texture analysis methods
usually are classified into four categories: statistical methods, structural,
model-based and transform-based methods. This paper discusses the various
methods used for texture or analysis in details. New researches shows the power
of combinational methods for texture analysis, which can't be in specific
category. This paper provides a review on well known combinational methods in a
specific section with details. This paper counts advantages and disadvantages
of well-known texture image descriptors in the result part. Main focus in all
of the survived methods is on discrimination performance, computational
complexity and resistance to challenges such as noise, rotation, etc. A brief
review is also made on the common classifiers used for texture image
classification. Also, a survey on texture image benchmark datasets is included.Comment: 29 Pages, Keywords: Texture Image, Texture Analysis, Texture
classification, Feature extraction, Image processing, Local Binary Patterns,
Benchmark texture image dataset
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Texture is a fundamental characteristic of many types of images, and texture
representation is one of the essential and challenging problems in computer
vision and pattern recognition which has attracted extensive research
attention. Since 2000, texture representations based on Bag of Words (BoW) and
on Convolutional Neural Networks (CNNs) have been extensively studied with
impressive performance. Given this period of remarkable evolution, this paper
aims to present a comprehensive survey of advances in texture representation
over the last two decades. More than 200 major publications are cited in this
survey covering different aspects of the research, which includes (i) problem
description; (ii) recent advances in the broad categories of BoW-based,
CNN-based and attribute-based methods; and (iii) evaluation issues,
specifically benchmark datasets and state of the art results. In retrospect of
what has been achieved so far, the survey discusses open challenges and
directions for future research.Comment: Accepted by IJC
LGLG-WPCA: An Effective Texture-based Method for Face Recognition
In this paper, we proposed an effective face feature extraction method by
Learning Gabor Log-Euclidean Gaussian with Whitening Principal Component
Analysis (WPCA), called LGLG-WPCA. The proposed method learns face features
from the embedded multivariate Gaussian in Gabor wavelet domain; it has the
robust performance to adverse conditions such as varying poses, skin aging and
uneven illumination. Because the space of Gaussian is a Riemannian manifold and
it is difficult to incorporate learning mechanism in the model. To address this
issue, we use L2EMG to map the multidimensional Gaussian model to the linear
space, and then use WPCA to learn face features. We also implemented the
key-point-based version of LGLG-WPCA, called LGLG(KP)-WPCA. Experiments show
the proposed methods are effective and promising for face texture feature
extraction and the combination of the feature of the proposed methods and the
features of Deep Convolutional Network (DCNN) achieved the best recognition
accuracies on FERET database compared to the state-of-the-art methods. In the
next version of this paper, we will test the performance of the proposed
methods on the large-varying pose databases
A Vision System for Multi-View Face Recognition
Multimodal biometric identification has been grown a great attention in the
most interests in the security fields. In the real world there exist modern
system devices that are able to detect, recognize, and classify the human
identities with reliable and fast recognition rates. Unfortunately most of
these systems rely on one modality, and the reliability for two or more
modalities are further decreased. The variations of face images with respect to
different poses are considered as one of the important challenges in face
recognition systems. In this paper, we propose a multimodal biometric system
that able to detect the human face images that are not only one view face
image, but also multi-view face images. Each subject entered to the system
adjusted their face at front of the three cameras, and then the features of the
face images are extracted based on Speeded Up Robust Features (SURF) algorithm.
We utilize Multi-Layer Perceptron (MLP) and combined classifiers based on both
Learning Vector Quantization (LVQ), and Radial Basis Function (RBF) for
classification purposes. The proposed system has been tested using SDUMLA-HMT,
and CASIA datasets. Furthermore, we collected a database of multi-view face
images by which we take the additive white Gaussian noise into considerations.
The results indicated the reliability, robustness of the proposed system with
different poses and variations including noise images.Comment: 7 pages, 4 figures, 4 table
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
Learning 2D Gabor Filters by Infinite Kernel Learning Regression
Gabor functions have wide-spread applications in image processing and
computer vision. In this paper, we prove that 2D Gabor functions are
translation-invariant positive-definite kernels and propose a novel formulation
for the problem of image representation with Gabor functions based on infinite
kernel learning regression. Using this formulation, we obtain a support vector
expansion of an image based on a mixture of Gabor functions. The problem with
this representation is that all Gabor functions are present at all support
vector pixels. Applying LASSO to this support vector expansion, we obtain a
sparse representation in which each Gabor function is positioned at a very
small set of pixels. As an application, we introduce a method for learning a
dataset-specific set of Gabor filters that can be used subsequently for feature
extraction. Our experiments show that use of the learned Gabor filters improves
the recognition accuracy of a recently introduced face recognition algorithm
PCANet: A Simple Deep Learning Baseline for Image Classification?
In this work, we propose a very simple deep learning network for image
classification which comprises only the very basic data processing components:
cascaded principal component analysis (PCA), binary hashing, and block-wise
histograms. In the proposed architecture, PCA is employed to learn multistage
filter banks. It is followed by simple binary hashing and block histograms for
indexing and pooling. This architecture is thus named as a PCA network (PCANet)
and can be designed and learned extremely easily and efficiently. For
comparison and better understanding, we also introduce and study two simple
variations to the PCANet, namely the RandNet and LDANet. They share the same
topology of PCANet but their cascaded filters are either selected randomly or
learned from LDA. We have tested these basic networks extensively on many
benchmark visual datasets for different tasks, such as LFW for face
verification, MultiPIE, Extended Yale B, AR, FERET datasets for face
recognition, as well as MNIST for hand-written digits recognition.
Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with
the state of the art features, either prefixed, highly hand-crafted or
carefully learned (by DNNs). Even more surprisingly, it sets new records for
many classification tasks in Extended Yale B, AR, FERET datasets, and MNIST
variations. Additional experiments on other public datasets also demonstrate
the potential of the PCANet serving as a simple but highly competitive baseline
for texture classification and object recognition
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