11,229 research outputs found
Face Recognition in Low Quality Images: A Survey
Low-resolution face recognition (LRFR) has received increasing attention over
the past few years. Its applications lie widely in the real-world environment
when high-resolution or high-quality images are hard to capture. One of the
biggest demands for LRFR technologies is video surveillance. As the the number
of surveillance cameras in the city increases, the videos that captured will
need to be processed automatically. However, those videos or images are usually
captured with large standoffs, arbitrary illumination condition, and diverse
angles of view. Faces in these images are generally small in size. Several
studies addressed this problem employed techniques like super resolution,
deblurring, or learning a relationship between different resolution domains. In
this paper, we provide a comprehensive review of approaches to low-resolution
face recognition in the past five years. First, a general problem definition is
given. Later, systematically analysis of the works on this topic is presented
by catogory. In addition to describing the methods, we also focus on datasets
and experiment settings. We further address the related works on unconstrained
low-resolution face recognition and compare them with the result that use
synthetic low-resolution data. Finally, we summarized the general limitations
and speculate a priorities for the future effort.Comment: There are some mistakes addressing in this paper which will be
misleading to the reader and we wont have a new version in short time. We
will resubmit once it is being corecte
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/
Modal Regression based Atomic Representation for Robust Face Recognition
Representation based classification (RC) methods such as sparse RC (SRC) have
shown great potential in face recognition in recent years. Most previous RC
methods are based on the conventional regression models, such as lasso
regression, ridge regression or group lasso regression. These regression models
essentially impose a predefined assumption on the distribution of the noise
variable in the query sample, such as the Gaussian or Laplacian distribution.
However, the complicated noises in practice may violate the assumptions and
impede the performance of these RC methods. In this paper, we propose a modal
regression based atomic representation and classification (MRARC) framework to
alleviate such limitation. Unlike previous RC methods, the MRARC framework does
not require the noise variable to follow any specific predefined distributions.
This gives rise to the capability of MRARC in handling various complex noises
in reality. Using MRARC as a general platform, we also develop four novel RC
methods for unimodal and multimodal face recognition, respectively. In
addition, we devise a general optimization algorithm for the unified MRARC
framework based on the alternating direction method of multipliers (ADMM) and
half-quadratic theory. The experiments on real-world data validate the efficacy
of MRARC for robust face recognition.Comment: 10 pages, 9 figure
HSR: L1/2 Regularized Sparse Representation for Fast Face Recognition using Hierarchical Feature Selection
In this paper, we propose a novel method for fast face recognition called
L1/2 Regularized Sparse Representation using Hierarchical Feature Selection
(HSR). By employing hierarchical feature selection, we can compress the scale
and dimension of global dictionary, which directly contributes to the decrease
of computational cost in sparse representation that our approach is strongly
rooted in. It consists of Gabor wavelets and Extreme Learning Machine
Auto-Encoder (ELM-AE) hierarchically. For Gabor wavelets part, local features
can be extracted at multiple scales and orientations to form Gabor-feature
based image, which in turn improves the recognition rate. Besides, in the
presence of occluded face image, the scale of Gabor-feature based global
dictionary can be compressed accordingly because redundancies exist in
Gabor-feature based occlusion dictionary. For ELM-AE part, the dimension of
Gabor-feature based global dictionary can be compressed because
high-dimensional face images can be rapidly represented by low-dimensional
feature. By introducing L1/2 regularization, our approach can produce sparser
and more robust representation compared to regularized Sparse Representation
based Classification (SRC), which also contributes to the decrease of the
computational cost in sparse representation. In comparison with related work
such as SRC and Gabor-feature based SRC (GSRC), experimental results on a
variety of face databases demonstrate the great advantage of our method for
computational cost. Moreover, we also achieve approximate or even better
recognition rate.Comment: Submitted to IEEE Computational Intelligence Magazine in 09/201
Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment
Single-sample face recognition is one of the most challenging problems in
face recognition. We propose a novel algorithm to address this problem based on
a sparse representation based classification (SRC) framework. The new algorithm
is robust to image misalignment and pixel corruption, and is able to reduce
required gallery images to one sample per class. To compensate for the missing
illumination information traditionally provided by multiple gallery images, a
sparse illumination learning and transfer (SILT) technique is introduced. The
illumination in SILT is learned by fitting illumination examples of auxiliary
face images from one or more additional subjects with a sparsely-used
illumination dictionary. By enforcing a sparse representation of the query
image in the illumination dictionary, the SILT can effectively recover and
transfer the illumination and pose information from the alignment stage to the
recognition stage. Our extensive experiments have demonstrated that the new
algorithms significantly outperform the state of the art in the single-sample
regime and with less restrictions. In particular, the single-sample face
alignment accuracy is comparable to that of the well-known Deformable SRC
algorithm using multiple gallery images per class. Furthermore, the face
recognition accuracy exceeds those of the SRC and Extended SRC algorithms using
hand labeled alignment initialization
Structured Occlusion Coding for Robust Face Recognition
Occlusion in face recognition is a common yet challenging problem. While
sparse representation based classification (SRC) has been shown promising
performance in laboratory conditions (i.e. noiseless or random pixel
corrupted), it performs much worse in practical scenarios. In this paper, we
consider the practical face recognition problem, where the occlusions are
predictable and available for sampling. We propose the structured occlusion
coding (SOC) to address occlusion problems. The structured coding here lies in
two folds. On one hand, we employ a structured dictionary for recognition. On
the other hand, we propose to use the structured sparsity in this formulation.
Specifically, SOC simultaneously separates the occlusion and classifies the
image. In this way, the problem of recognizing an occluded image is turned into
seeking a structured sparse solution on occlusion-appended dictionary. In order
to construct a well-performing occlusion dictionary, we propose an occlusion
mask estimating technique via locality constrained dictionary (LCD), showing
striking improvement in occlusion sample. On a category-specific occlusion
dictionary, we replace norm sparsity with the structured sparsity which is
shown more robust, further enhancing the robustness of our approach. Moreover,
SOC achieves significant improvement in handling large occlusion in real world.
Extensive experiments are conducted on public data sets to validate the
superiority of the proposed algorithm
Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture
We propose a novel couple mappings method for low resolution face recognition
using deep convolutional neural networks (DCNNs). The proposed architecture
consists of two branches of DCNNs to map the high and low resolution face
images into a common space with nonlinear transformations. The branch
corresponding to transformation of high resolution images consists of 14 layers
and the other branch which maps the low resolution face images to the common
space includes a 5-layer super-resolution network connected to a 14-layer
network. The distance between the features of corresponding high and low
resolution images are backpropagated to train the networks. Our proposed method
is evaluated on FERET data set and compared with state-of-the-art competing
methods. Our extensive experimental results show that the proposed method
significantly improves the recognition performance especially for very low
resolution probe face images (11.4% improvement in recognition accuracy).
Furthermore, it can reconstruct a high resolution image from its corresponding
low resolution probe image which is comparable with state-of-the-art
super-resolution methods in terms of visual quality.Comment: 11 pages, 8 figure
Robust Face Recognition by Constrained Part-based Alignment
Developing a reliable and practical face recognition system is a
long-standing goal in computer vision research. Existing literature suggests
that pixel-wise face alignment is the key to achieve high-accuracy face
recognition. By assuming a human face as piece-wise planar surfaces, where each
surface corresponds to a facial part, we develop in this paper a Constrained
Part-based Alignment (CPA) algorithm for face recognition across pose and/or
expression. Our proposed algorithm is based on a trainable CPA model, which
learns appearance evidence of individual parts and a tree-structured shape
configuration among different parts. Given a probe face, CPA simultaneously
aligns all its parts by fitting them to the appearance evidence with
consideration of the constraint from the tree-structured shape configuration.
This objective is formulated as a norm minimization problem regularized by
graph likelihoods. CPA can be easily integrated with many existing classifiers
to perform part-based face recognition. Extensive experiments on benchmark face
datasets show that CPA outperforms or is on par with existing methods for
robust face recognition across pose, expression, and/or illumination changes
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
Collaborative Representation Classification Ensemble for Face Recognition
Collaborative Representation Classification (CRC) for face recognition
attracts a lot attention recently due to its good recognition performance and
fast speed. Compared to Sparse Representation Classification (SRC), CRC
achieves a comparable recognition performance with 10-1000 times faster speed.
In this paper, we propose to ensemble several CRC models to promote the
recognition rate, where each CRC model uses different and divergent randomly
generated biologically-inspired features as the face representation. The
proposed ensemble algorithm calculates an ensemble weight for each CRC model
that guided by the underlying classification rule of CRC. The obtained weights
reflect the confidences of those CRC models where the more confident CRC models
have larger weights. The proposed weighted ensemble method proves to be very
effective and improves the performance of each CRC model significantly.
Extensive experiments are conducted to show the superior performance of the
proposed method.Comment: 6 page
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