6,443 research outputs found
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
Feature Selection via Sparse Approximation for Face Recognition
Inspired by biological vision systems, the over-complete local features with
huge cardinality are increasingly used for face recognition during the last
decades. Accordingly, feature selection has become more and more important and
plays a critical role for face data description and recognition. In this paper,
we propose a trainable feature selection algorithm based on the regularized
frame for face recognition. By enforcing a sparsity penalty term on the minimum
squared error (MSE) criterion, we cast the feature selection problem into a
combinatorial sparse approximation problem, which can be solved by greedy
methods or convex relaxation methods. Moreover, based on the same frame, we
propose a sparse Ho-Kashyap (HK) procedure to obtain simultaneously the optimal
sparse solution and the corresponding margin vector of the MSE criterion. The
proposed methods are used for selecting the most informative Gabor features of
face images for recognition and the experimental results on benchmark face
databases demonstrate the effectiveness of the proposed methods
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
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
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/
Face Identification with Second-Order Pooling
Automatic face recognition has received significant performance improvement
by developing specialised facial image representations. On the other hand,
generic object recognition has rarely been applied to the face recognition.
Spatial pyramid pooling of features encoded by an over-complete dictionary has
been the key component of many state-of-the-art image classification systems.
Inspired by its success, in this work we develop a new face image
representation method inspired by the second-order pooling in Carreira et al.
[1], which was originally proposed for image segmentation. The proposed method
differs from the previous methods in that, we encode the densely extracted
local patches by a small-size dictionary; and the facial image signatures are
obtained by pooling the second-order statistics of the encoded features. We
show the importance of pooling on encoded features, which is bypassed by the
original second-order pooling method to avoid the high computational cost.
Equipped with a simple linear classifier, the proposed method outperforms the
state-of-the-art face identification performance by large margins. For example,
on the LFW databases, the proposed method performs better than the previous
best by around 13% accuracy.Comment: 9 page
Modeling of Facial Aging and Kinship: A Survey
Computational facial models that capture properties of facial cues related to
aging and kinship increasingly attract the attention of the research community,
enabling the development of reliable methods for age progression, age
estimation, age-invariant facial characterization, and kinship verification
from visual data. In this paper, we review recent advances in modeling of
facial aging and kinship. In particular, we provide an up-to date, complete
list of available annotated datasets and an in-depth analysis of geometric,
hand-crafted, and learned facial representations that are used for facial aging
and kinship characterization. Moreover, evaluation protocols and metrics are
reviewed and notable experimental results for each surveyed task are analyzed.
This survey allows us to identify challenges and discuss future research
directions for the development of robust facial models in real-world
conditions
Large Margin Low Rank Tensor Analysis
Other than vector representations, the direct objects of human cognition are
generally high-order tensors, such as 2D images and 3D textures. From this
fact, two interesting questions naturally arise: How does the human brain
represent these tensor perceptions in a "manifold" way, and how can they be
recognized on the "manifold"? In this paper, we present a supervised model to
learn the intrinsic structure of the tensors embedded in a high dimensional
Euclidean space. With the fixed point continuation procedures, our model
automatically and jointly discovers the optimal dimensionality and the
representations of the low dimensional embeddings. This makes it an effective
simulation of the cognitive process of human brain. Furthermore, the
generalization of our model based on similarity between the learned low
dimensional embeddings can be viewed as counterpart of recognition of human
brain. Experiments on applications for object recognition and face recognition
demonstrate the superiority of our proposed model over state-of-the-art
approaches.Comment: 30 page
Cross-pose Face Recognition by Canonical Correlation Analysis
The pose problem is one of the bottlenecks in automatic face recognition. We
argue that one of the diffculties in this problem is the severe misalignment in
face images or feature vectors with different poses. In this paper, we propose
that this problem can be statistically solved or at least mitigated by
maximizing the intra-subject across-pose correlations via canonical correlation
analysis (CCA). In our method, based on the data set with coupled face images
of the same identities and across two different poses, CCA learns
simultaneously two linear transforms, each for one pose. In the transformed
subspace, the intra-subject correlations between the different poses are
maximized, which implies pose-invariance or pose-robustness is achieved. The
experimental results show that our approach could considerably improve the
recognition performance. And if further enhanced with holistic+local feature
representation, the performance could be comparable to the state-of-the-art
Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition
In this paper, we present a novel approach to automatic 3D Facial Expression
Recognition (FER) based on deep representation of facial 3D geometric and 2D
photometric attributes. A 3D face is firstly represented by its geometric and
photometric attributes, including the geometry map, normal maps, normalized
curvature map and texture map. These maps are then fed into a pre-trained deep
convolutional neural network to generate the deep representation. Then the
facial expression prediction is simplyachieved by training linear SVMs over the
deep representation for different maps and fusing these SVM scores. The
visualizations show that the deep representation provides a complete and highly
discriminative coding scheme for 3D faces. Comprehensive experiments on the
BU-3DFE database demonstrate that the proposed deep representation can
outperform the widely used hand-crafted descriptors (i.e., LBP, SIFT, HOG,
Gabor) and the state-of-art approaches under the same experimental protocols
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