pwc-archive/papers-with-abstracts huggingface dataset:
Face Recognition Using Discrete Cosine Transform for Global and Local Features
Authors: Aman R. Chadha, Pallavi P. Vaidya, M. Mani Roja
Date: 2011-11-06 00:00:00
Tasks: Face Recognition

Face Recognition using Discrete Cosine Transform (DCT) for Local and Global
Features involves recognizing the corresponding face image from the database.
The face image obtained from the user is cropped such that only the frontal
face image is extracted, eliminating the background. The image is restricted to
a size of 128 x 128 pixels. All images in the database are gray level images.
DCT is applied to the entire image. This gives DCT coefficients, which are
global features. Local features such as eyes, nose and mouth are also extracted
and DCT is applied to these features. Depending upon the recognition rate
obtained for each feature, they are given weightage and then combined. Both
local and global features are used for comparison. By comparing the ranks for
global and local features, the false acceptance rate for DCT can be minimized.
Discriminative Local Sparse Representations for Robust Face Recognition
Authors: Yi Chen, Umamahesh Srinivas, Thong T. Do, Vishal Monga, Trac. D. Tran
Date: 2011-11-08 00:00:00
Tasks: Face Recognition, General Classification, Robust Face Recognition

A key recent advance in face recognition models a test face image as a sparse
linear combination of a set of training face images. The resulting sparse
representations have been shown to possess robustness against a variety of
distortions like random pixel corruption, occlusion and disguise. This approach
however makes the restrictive (in many scenarios) assumption that test faces
must be perfectly aligned (or registered) to the training data prior to
classification. In this paper, we propose a simple yet robust local block-based
sparsity model, using adaptively-constructed dictionaries from local features
in the training data, to overcome this misalignment problem. Our approach is
inspired by human perception: we analyze a series of local discriminative
features and combine them to arrive at the final classification decision. We
propose a probabilistic graphical model framework to explicitly mine the
conditional dependencies between these distinct sparse local features. In
particular, we learn discriminative graphs on sparse representations obtained
from distinct local slices of a face. Conditional correlations between these
sparse features are first discovered (in the training phase), and subsequently
exploited to bring about significant improvements in recognition rates.
Experimental results obtained on benchmark face databases demonstrate the
effectiveness of the proposed algorithms in the presence of multiple
registration errors (such as translation, rotation, and scaling) as well as
under variations of pose and illumination.
Examplers based image fusion features for face recognition
Authors: Alex Pappachen James, Sima Dimitrijev
Date: 2012-01-28 00:00:00
Tasks: Face Recognition, General Classification

Examplers of a face are formed from multiple gallery images of a person and
are used in the process of classification of a test image. We incorporate such
examplers in forming a biologically inspired local binary decisions on
similarity based face recognition method. As opposed to single model approaches
such as face averages the exampler based approach results in higher recognition
accu- racies and stability. Using multiple training samples per person, the
method shows the following recognition accuracies: 99.0% on AR, 99.5% on FERET,
99.5% on ORL, 99.3% on EYALE, 100.0% on YALE and 100.0% on CALTECH face
databases. In addition to face recognition, the method also detects the natural
variability in the face images which can find application in automatic tagging
of face images.
Fully Automatic Expression-Invariant Face Correspondence
Authors: Augusto Salazar, Stefanie Wuhrer, Chang Shu, Flavio Prieto
Date: 2012-02-07 00:00:00

We consider the problem of computing accurate point-to-point correspondences
among a set of human face scans with varying expressions. Our fully automatic
approach does not require any manually placed markers on the scan. Instead, the
approach learns the locations of a set of landmarks present in a database and
uses this knowledge to automatically predict the locations of these landmarks
on a newly available scan. The predicted landmarks are then used to compute
point-to-point correspondences between a template model and the newly available
scan. To accurately fit the expression of the template to the expression of the
scan, we use as template a blendshape model. Our algorithm was tested on a
database of human faces of different ethnic groups with strongly varying
expressions. Experimental results show that the obtained point-to-point
correspondence is both highly accurate and consistent for most of the tested 3D
face models.
Regularized Robust Coding for Face Recognition
Authors: Meng Yang, Lei Zhang, Jian Yang, David Zhang
Date: 2012-02-20 00:00:00
Tasks: Face Recognition, Robust Face Recognition, Sparse Representation-based Classification

Recently the sparse representation based classification (SRC) has been
proposed for robust face recognition (FR). In SRC, the testing image is coded
as a sparse linear combination of the training samples, and the representation
fidelity is measured by the l2-norm or l1-norm of the coding residual. Such a
sparse coding model assumes that the coding residual follows Gaussian or
Laplacian distribution, which may not be effective enough to describe the
coding residual in practical FR systems. Meanwhile, the sparsity constraint on
the coding coefficients makes SRC's computational cost very high. In this
paper, we propose a new face coding model, namely regularized robust coding
(RRC), which could robustly regress a given signal with regularized regression
coefficients. By assuming that the coding residual and the coding coefficient
are respectively independent and identically distributed, the RRC seeks for a
maximum a posterior solution of the coding problem. An iteratively reweighted
regularized robust coding (IR3C) algorithm is proposed to solve the RRC model
efficiently. Extensive experiments on representative face databases demonstrate
that the RRC is much more effective and efficient than state-of-the-art sparse
representation based methods in dealing with face occlusion, corruption,
lighting and expression changes, etc.
Face Expression Recognition and Analysis: The State of the Art
Authors: Vinay Bettadapura
Date: 2012-03-30 00:00:00
Tasks: Face Detection

The automatic recognition of facial expressions has been an active research
topic since the early nineties. There have been several advances in the past
few years in terms of face detection and tracking, feature extraction
mechanisms and the techniques used for expression classification. This paper
surveys some of the published work since 2001 till date. The paper presents a
time-line view of the advances made in this field, the applications of
automatic face expression recognizers, the characteristics of an ideal system,
the databases that have been used and the advances made in terms of their
standardization and a detailed summary of the state of the art. The paper also
discusses facial parameterization using FACS Action Units (AUs) and MPEG-4
Facial Animation Parameters (FAPs) and the recent advances in face detection,
tracking and feature extraction methods. Notes have also been presented on
emotions, expressions and facial features, discussion on the six prototypic
expressions and the recent studies on expression classifiers. The paper ends
with a note on the challenges and the future work. This paper has been written
in a tutorial style with the intention of helping students and researchers who
are new to this field.
DBC based Face Recognition using DWT
Authors: H S Jagadeesh, K Suresh Babu, K B Raja
Date: 2012-05-08 00:00:00
Tasks: Face Recognition

The applications using face biometric has proved its reliability in last
decade. In this paper, we propose DBC based Face Recognition using DWT (DBC-
FR) model. The Poly-U Near Infra Red (NIR) database images are scanned and
cropped to get only the face part in pre-processing. The face part is resized
to 100*100 and DWT is applied to derive LL, LH, HL and HH subbands. The LL
subband of size 50*50 is converted into 100 cells with 5*5 dimention of each
cell. The Directional Binary Code (DBC) is applied on each 5*5 cell to derive
100 features. The Euclidian distance measure is used to compare the features of
test image and database images. The proposed algorithm render better percentage
recognition rate compared to the existing algorithm.
Pilgrims Face Recognition Dataset -- HUFRD
Authors: Salah A. Aly
Date: 2012-05-20 00:00:00
Tasks: Face Recognition

In this work, we define a new pilgrims face recognition dataset, called HUFRD
dataset. The new developed dataset presents various pilgrims' images taken from
outside the Holy Masjid El-Harram in Makkah during the 2011-2012 Hajj and Umrah
seasons. Such dataset will be used to test our developed facial recognition and
detection algorithms, as well as assess in the missing and found recognition
system \cite{crowdsensing}.
Optimizing Face Recognition Using PCA
Authors: Manal Abdullah, Majda Wazzan, Sahar Bo-saeed
Date: 2012-06-07 00:00:00
Tasks: Face Recognition

Principle Component Analysis PCA is a classical feature extraction and data
representation technique widely used in pattern recognition. It is one of the
most successful techniques in face recognition. But it has drawback of high
computational especially for big size database. This paper conducts a study to
optimize the time complexity of PCA (eigenfaces) that does not affects the
recognition performance. The authors minimize the participated eigenvectors
which consequently decreases the computational time. A comparison is done to
compare the differences between the recognition time in the original algorithm
and in the enhanced algorithm. The performance of the original and the enhanced
proposed algorithm is tested on face94 face database. Experimental results show
that the recognition time is reduced by 35% by applying our proposed enhanced
algorithm. DET Curves are used to illustrate the experimental results.
Face Recognition Algorithms based on Transformed Shape Features
Authors: Sambhunath Biswas, Amrita Biswas
Date: 2012-07-11 00:00:00
Tasks: Face Recognition

Human face recognition is, indeed, a challenging task, especially under the
illumination and pose variations. We examine in the present paper effectiveness
of two simple algorithms using coiflet packet and Radon transforms to recognize
human faces from some databases of still gray level images, under the
environment of illumination and pose variations. Both the algorithms convert
2-D gray level training face images into their respective depth maps or
physical shape which are subsequently transformed by Coiflet packet and Radon
transforms to compute energy for feature extraction. Experiments show that such
transformed shape features are robust to illumination and pose variations. With
the features extracted, training classes are optimally separated through linear
discriminant analysis (LDA), while classification for test face images is made
through a k-NN classifier, based on L1 norm and Mahalanobis distance measures.
Proposed algorithms are then tested on face images that differ in
illumination,expression or pose separately, obtained from three
databases,namely, ORL, Yale and Essex-Grimace databases. Results, so obtained,
are compared with two different existing algorithms.Performance using
Daubechies wavelets is also examined. It is seen that the proposed Coiflet
packet and Radon transform based algorithms have significant performance,
especially under different illumination conditions and pose variation.
Comparison shows the proposed algorithms are superior.
An Efficient Color Face Verification Based on 2-Directional 2-Dimensional Feature Extraction
Authors: Lan-Ting LI
Date: 2012-09-20 00:00:00
Tasks: Decision Making, Face Verification

A novel and uniform framework for face verification is presented in this
paper. First of all, a 2-directional 2-dimensional feature extraction method is
adopted to extract client-specific template - 2D discrimant projection matrix.
Then the face skin color information is utilized as an additive feature to
enhance decision making strategy that makes use of not only 2D grey feature but
also 2D skin color feature. A fusion decision of both is applied to experiment
the performance on the XM2VTS database according to Lausanne protocol.
Experimental results show that the framework achieves high verification
accuracy and verification speed.
Robust Degraded Face Recognition Using Enhanced Local Frequency Descriptor and Multi-scale Competition
Authors: Guangling Sun, Guoqing Li, Xinpeng Zhang
Date: 2012-10-03 00:00:00
Tasks: Descriptive, Face Recognition

Recognizing degraded faces from low resolution and blurred images are common
yet challenging task. Local Frequency Descriptor (LFD) has been proved to be
effective for this task yet it is extracted from a spatial neighborhood of a
pixel of a frequency plane independently regardless of correlations between
frequencies. In addition, it uses a fixed window size named single scale of
short-term Frequency transform (STFT). To explore the frequency correlations
and preserve low resolution and blur insensitive simultaneously, we propose
Enhanced LFD in which information in space and frequency is jointly utilized so
as to be more descriptive and discriminative than LFD. The multi-scale
competition strategy that extracts multiple descriptors corresponding to
multiple window sizes of STFT and take one corresponding to maximum confidence
as the final recognition result. The experiments conducted on Yale and FERET
databases demonstrate that promising results have been achieved by the proposed
Enhanced LFD and multi-scale competition strategy.
A comparative study on face recognition techniques and neural network
Authors: Meftah Ur Rahman
Date: 2012-10-06 00:00:00
Tasks: Face Recognition

In modern times, face recognition has become one of the key aspects of
computer vision. There are at least two reasons for this trend; the first is
the commercial and law enforcement applications, and the second is the
availability of feasible technologies after years of research. Due to the very
nature of the problem, computer scientists, neuro-scientists and psychologists
all share a keen interest in this field. In plain words, it is a computer
application for automatically identifying a person from a still image or video
frame. One of the ways to accomplish this is by comparing selected features
from the image and a facial database. There are hundreds if not thousand
factors associated with this. In this paper some of the most common techniques
available including applications of neural network in facial recognition are
studied and compared with respect to their performance.
3D Face Recognition using Significant Point based SULD Descriptor
Authors: B. H. Shekar, N. Harivinod, M. Sharmila Kumari, K. Raghurama Holla
Date: 2012-10-26 00:00:00
Tasks: Face Recognition

In this work, we present a new 3D face recognition method based on Speeded-Up
Local Descriptor (SULD) of significant points extracted from the range images
of faces. The proposed model consists of a method for extracting distinctive
invariant features from range images of faces that can be used to perform
reliable matching between different poses of range images of faces. For a given
3D face scan, range images are computed and the potential interest points are
identified by searching at all scales. Based on the stability of the interest
point, significant points are extracted. For each significant point we compute
the SULD descriptor which consists of vector made of values from the convolved
Haar wavelet responses located on concentric circles centred on the significant
point, and where the amount of Gaussian smoothing is proportional to the radii
of the circles. Experimental results show that the newly proposed method
provides higher recognition rate compared to other existing contemporary models
developed for 3D face recognition.
Robust Face Recognition using Local Illumination Normalization and Discriminant Feature Point Selection
Authors: Song Han, Jinsong Kim, Cholhun Kim, Jongchol Jo, Sunam Han
Date: 2012-12-11 00:00:00
Tasks: Face Detection, Face Recognition, Robust Face Recognition

Face recognition systems must be robust to the variation of various factors
such as facial expression, illumination, head pose and aging. Especially, the
robustness against illumination variation is one of the most important problems
to be solved for the practical use of face recognition systems. Gabor wavelet
is widely used in face detection and recognition because it gives the
possibility to simulate the function of human visual system. In this paper, we
propose a method for extracting Gabor wavelet features which is stable under
the variation of local illumination and show experiment results demonstrating
its effectiveness.
Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval
Authors: Chang Huang, Shenghuo Zhu, Kai Yu
Date: 2012-12-25 00:00:00
Tasks: Face Verification, Metric Learning, Retrieval, Triplet

Learning Mahanalobis distance metrics in a high- dimensional feature space is
very difficult especially when structural sparsity and low rank are enforced to
improve com- putational efficiency in testing phase. This paper addresses both
aspects by an ensemble metric learning approach that consists of sparse block
diagonal metric ensembling and join- t metric learning as two consecutive
steps. The former step pursues a highly sparse block diagonal metric by
selecting effective feature groups while the latter one further exploits
correlations between selected feature groups to obtain an accurate and low rank
metric. Our algorithm considers all pairwise or triplet constraints generated
from training samples with explicit class labels, and possesses good scala-
bility with respect to increasing feature dimensionality and growing data
volumes. Its applications to face verification and retrieval outperform
existing state-of-the-art methods in accuracy while retaining high efficiency.
Fast Matching by 2 Lines of Code for Large Scale Face Recognition Systems
Authors: Dong Yi, Zhen Lei, Yang Hu, Stan Z. Li
Date: 2013-02-28 00:00:00
Tasks: Computational Efficiency, Face Recognition

In this paper, we propose a method to apply the popular cascade classifier
into face recognition to improve the computational efficiency while keeping
high recognition rate. In large scale face recognition systems, because the
probability of feature templates coming from different subjects is very high,
most of the matching pairs will be rejected by the early stages of the cascade.
Therefore, the cascade can improve the matching speed significantly. On the
other hand, using the nested structure of the cascade, we could drop some
stages at the end of feature to reduce the memory and bandwidth usage in some
resources intensive system while not sacrificing the performance too much. The
cascade is learned by two steps. Firstly, some kind of prepared features are
grouped into several nested stages. And then, the threshold of each stage is
learned to achieve user defined verification rate (VR). In the paper, we take a
landmark based Gabor+LDA face recognition system as baseline to illustrate the
process and advantages of the proposed method. However, the use of this method
is very generic and not limited in face recognition, which can be easily
generalized to other biometrics as a post-processing module. Experiments on the
FERET database show the good performance of our baseline and an experiment on a
self-collected large scale database illustrates that the cascade can improve
the matching speed significantly.
On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly
Authors: Yongkang Wong, Mehrtash T. Harandi, Conrad Sanderson
Date: 2013-03-07 00:00:00
Tasks: Face Recognition, Face Verification, Robust Face Recognition

In the field of face recognition, Sparse Representation (SR) has received
considerable attention during the past few years. Most of the relevant
literature focuses on holistic descriptors in closed-set identification
applications. The underlying assumption in SR-based methods is that each class
in the gallery has sufficient samples and the query lies on the subspace
spanned by the gallery of the same class. Unfortunately, such assumption is
easily violated in the more challenging face verification scenario, where an
algorithm is required to determine if two faces (where one or both have not
been seen before) belong to the same person. In this paper, we first discuss
why previous attempts with SR might not be applicable to verification problems.
We then propose an alternative approach to face verification via SR.
Specifically, we propose to use explicit SR encoding on local image patches
rather than the entire face. The obtained sparse signals are pooled via
averaging to form multiple region descriptors, which are then concatenated to
form an overall face descriptor. Due to the deliberate loss spatial relations
within each region (caused by averaging), the resulting descriptor is robust to
misalignment & various image deformations. Within the proposed framework, we
evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder
Neural Network (SANN), and an implicit probabilistic technique based on
Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and
ChokePoint datasets show that the proposed local SR approach obtains
considerably better and more robust performance than several previous
state-of-the-art holistic SR methods, in both verification and closed-set
identification problems. The experiments also show that l1-minimisation based
encoding has a considerably higher computational than the other techniques, but
leads to higher recognition rates.
Combined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification
Authors: Conrad Sanderson, Mehrtash T. Harandi, Yongkang Wong, Brian C. Lovell
Date: 2013-03-12 00:00:00
Tasks: Face Recognition, Face Verification, set matching

In contrast to comparing faces via single exemplars, matching sets of face
images increases robustness and discrimination performance. Recent image set
matching approaches typically measure similarities between subspaces or
manifolds, while representing faces in a rigid and holistic manner. Such
representations are easily affected by variations in terms of alignment,
illumination, pose and expression. While local feature based representations
are considerably more robust to such variations, they have received little
attention within the image set matching area. We propose a novel image set
matching technique, comprised of three aspects: (i) robust descriptors of face
regions based on local features, partly inspired by the hierarchy in the human
visual system, (ii) use of several subspace and exemplar metrics to compare
corresponding face regions, (iii) jointly learning which regions are the most
discriminative while finding the optimal mixing weights for combining metrics.
Face recognition experiments on LFW, PIE and MOBIO face datasets show that the
proposed algorithm obtains considerably better performance than several recent
state-of-the-art techniques, such as Local Principal Angle and the Kernel
Affine Hull Method.