1,733 research outputs found
An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements
Spectral imaging has recently gained traction for face recognition in
biometric systems. We investigate the merits of spectral imaging for face
recognition and the current challenges that hamper the widespread deployment of
spectral sensors for face recognition. The reliability of conventional face
recognition systems operating in the visible range is compromised by
illumination changes, pose variations and spoof attacks. Recent works have
reaped the benefits of spectral imaging to counter these limitations in
surveillance activities (defence, airport security checks, etc.). However, the
implementation of this technology for biometrics, is still in its infancy due
to multiple reasons. We present an overview of the existing work in the domain
of spectral imaging for face recognition, different types of modalities and
their assessment, availability of public databases for sake of reproducible
research as well as evaluation of algorithms, and recent advancements in the
field, such as, the use of deep learning-based methods for recognizing faces
from spectral images
Multispectral Palmprint Encoding and Recognition
Palmprints are emerging as a new entity in multi-modal biometrics for human
identification and verification. Multispectral palmprint images captured in the
visible and infrared spectrum not only contain the wrinkles and ridge structure
of a palm, but also the underlying pattern of veins; making them a highly
discriminating biometric identifier. In this paper, we propose a feature
encoding scheme for robust and highly accurate representation and matching of
multispectral palmprints. To facilitate compact storage of the feature, we
design a binary hash table structure that allows for efficient matching in
large databases. Comprehensive experiments for both identification and
verification scenarios are performed on two public datasets -- one captured
with a contact-based sensor (PolyU dataset), and the other with a contact-free
sensor (CASIA dataset). Recognition results in various experimental setups show
that the proposed method consistently outperforms existing state-of-the-art
methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA)
are the lowest reported in literature on both dataset and clearly indicate the
viability of palmprint as a reliable and promising biometric. All source codes
are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z.
Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral
Palmprint Encoding for Human Recognition", International Conference on
Computer Vision, 2011. MATLAB Code available:
https://sites.google.com/site/zohaibnet/Home/code
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES
A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. This method involves mainly in four steps at the various stages. The spectral and spatial information reflected from the original Hyperspectral Images with four various features. A shape adaptive (SA) spatial region is obtained in each pixel region at the second step. The algorithm namely sparse representation has applied to get the coefficients of sparse for each shape adaptive region in the form of matrix with multiple features. For each test pixel, the class label is determined with the help of obtained coefficients. The performances of MFASR have much better classification results than other classifiers in the terms of quantitative and qualitative percentage of results. This MFASR will make benefit of strong correlations that are obtained from different extracted features and this make use of effective features and effective adaptive sparse representation. Thus, the very high classification performance was achieved through this MFASR technique
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification
Hyperspectral image (HSI) classification, which aims to assign an accurate
label for hyperspectral pixels, has drawn great interest in recent years.
Although low rank representation (LRR) has been used to classify HSI, its
ability to segment each class from the whole HSI data has not been exploited
fully yet. LRR has a good capacity to capture the underlying lowdimensional
subspaces embedded in original data. However, there are still two drawbacks for
LRR. First, LRR does not consider the local geometric structure within data,
which makes the local correlation among neighboring data easily ignored.
Second, the representation obtained by solving LRR is not discriminative enough
to separate different data. In this paper, a novel locality and structure
regularized low rank representation (LSLRR) model is proposed for HSI
classification. To overcome the above limitations, we present locality
constraint criterion (LCC) and structure preserving strategy (SPS) to improve
the classical LRR. Specifically, we introduce a new distance metric, which
combines both spatial and spectral features, to explore the local similarity of
pixels. Thus, the global and local structures of HSI data can be exploited
sufficiently. Besides, we propose a structure constraint to make the
representation have a near block-diagonal structure. This helps to determine
the final classification labels directly. Extensive experiments have been
conducted on three popular HSI datasets. And the experimental results
demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.Comment: 14 pages, 7 figures, TGRS201
Spectral-spatial Feature Extraction for Hyperspectral Image Classification
As an emerging technology, hyperspectral imaging provides huge
opportunities in both remote sensing and computer vision. The
advantage of hyperspectral imaging comes from the high resolution
and wide range in the electromagnetic spectral domain which
reflects the intrinsic properties of object materials. By
combining spatial and spectral information, it is possible to
extract more comprehensive and discriminative representation for
objects of interest than traditional methods, thus facilitating
the basic pattern recognition tasks, such as object detection,
recognition, and classification. With advanced imaging
technologies gradually available for universities and industry,
there is an increased demand to develop new methods which can
fully explore the information embedded in hyperspectral images.
In this thesis, three spectral-spatial feature extraction methods
are developed for salient object detection, hyperspectral face
recognition, and remote sensing image classification.
Object detection is an important task for many applications based
on hyperspectral imaging. While most traditional methods rely on
the pixel-wise spectral response, many recent efforts have been
put on extracting spectral-spatial features. In the first
approach, we extend Itti's visual saliency model to the spectral
domain and introduce the spectral-spatial distribution based
saliency model for object detection. This procedure enables the
extraction of salient spectral features in the scale space, which
is related to the material property and spatial layout of
objects.
Traditional 2D face recognition has been studied for many years
and achieved great success. Nonetheless, there is high demand to
explore unrevealed information other than structures and textures
in spatial domain in faces. Hyperspectral imaging meets such
requirements by providing additional spectral information on
objects, in completion to the traditional spatial features
extracted in 2D images. In the second approach, we propose a
novel 3D high-order texture pattern descriptor for hyperspectral
face recognition, which effectively exploits both spatial and
spectral features in hyperspectral images. Based on the local
derivative pattern, our method encodes hyperspectral faces with
multi-directional derivatives and binarization function in
spectral-spatial space. Compared to traditional face recognition
methods, our method can describe distinctive micro-patterns which
integrate the spatial and spectral information of faces.
Mathematical morphology operations are limited to extracting
spatial feature in two-dimensional data and cannot cope with
hyperspectral images due to so-called ordering problem. In the
third approach, we propose a novel multi-dimensional morphology
descriptor, tensor morphology profile~(TMP), for hyperspectral
image classification. TMP is a general framework to extract
multi-dimensional structures in high-dimensional data. The
n-order morphology profile is proposed to work with the n-order
tensor, which can capture the inner high order structures. By
treating a hyperspectral image as a tensor, it is possible to
extend the morphology to high dimensional data so that powerful
morphological tools can be used to analyze hyperspectral images
with fused spectral-spatial information.
At last, we discuss the sampling strategy for the evaluation of
spectral-spatial methods in remote sensing hyperspectral image
classification. We find that traditional pixel-based random
sampling strategy for spectral processing will lead to unfair or
biased performance evaluation in the spectral-spatial processing
context. When training and testing samples are randomly drawn
from the same image, the dependence caused by overlap between
them may be artificially enhanced by some spatial processing
methods. It is hard to determine whether the improvement of
classification accuracy is caused by incorporating spatial
information into the classifier or by increasing the overlap
between training and testing samples. To partially solve this
problem, we propose a novel controlled random sampling strategy
for spectral-spatial methods. It can significantly reduce the
overlap between training and testing samples and provides more
objective and accurate evaluation
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