pwc-archive/papers-with-abstracts huggingface dataset:
Image Pixel Fusion for Human Face Recognition
Authors: Mrinal Kanti Bhowmik, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, Mahantapas Kundu
Date: 2010-07-05 00:00:00
Tasks: Face Recognition, Object Tracking

In this paper we present a technique for fusion of optical and thermal face
images based on image pixel fusion approach. Out of several factors, which
affect face recognition performance in case of visual images, illumination
changes are a significant factor that needs to be addressed. Thermal images are
better in handling illumination conditions but not very consistent in capturing
texture details of the faces. Other factors like sunglasses, beard, moustache
etc also play active role in adding complicacies to the recognition process.
Fusion of thermal and visual images is a solution to overcome the drawbacks
present in the individual thermal and visual face images. Here fused images are
projected into an eigenspace and the projected images are classified using a
radial basis function (RBF) neural network and also by a multi-layer perceptron
(MLP). In the experiments Object Tracking and Classification Beyond Visible
Spectrum (OTCBVS) database benchmark for thermal and visual face images have
been used. Comparison of experimental results show that the proposed approach
performs significantly well in recognizing face images with a success rate of
96% and 95.07% for RBF Neural Network and MLP respectively.
Classification of Fused Images using Radial Basis Function Neural Network for Human Face Recognition
Authors: M. K. Bhowmik, Debotosh Bhattacharjee, M. Nasipuri, D. K. Basu, M. Kundu
Date: 2010-07-05 00:00:00
Tasks: Face Recognition, General Classification, Object Tracking

Here an efficient fusion technique for automatic face recognition has been
presented. Fusion of visual and thermal images has been done to take the
advantages of thermal images as well as visual images. By employing fusion a
new image can be obtained, which provides the most detailed, reliable, and
discriminating information. In this method fused images are generated using
visual and thermal face images in the first step. In the second step, fused
images are projected into eigenspace and finally classified using a radial
basis function neural network. In the experiments Object Tracking and
Classification Beyond Visible Spectrum (OTCBVS) database benchmark for thermal
and visual face images have been used. Experimental results show that the
proposed approach performs well in recognizing unknown individuals with a
maximum success rate of 96%.
Human Face Recognition using Line Features
Authors: Mrinal Kanti Bhowmik, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, Mahantapas Kundu
Date: 2010-07-05 00:00:00
Tasks: Dimensionality Reduction, Face Recognition, Object Tracking

In this work we investigate a novel approach to handle the challenges of face
recognition, which includes rotation, scale, occlusion, illumination etc. Here,
we have used thermal face images as those are capable to minimize the affect of
illumination changes and occlusion due to moustache, beards, adornments etc.
The proposed approach registers the training and testing thermal face images in
polar coordinate, which is capable to handle complicacies introduced by scaling
and rotation. Line features are extracted from thermal polar images and feature
vectors are constructed using these line. Feature vectors thus obtained passes
through principal component analysis (PCA) for the dimensionality reduction of
feature vectors. Finally, the images projected into eigenspace are classified
using a multi-layer perceptron. In the experiments we have used Object Tracking
and Classification Beyond Visible Spectrum (OTCBVS) database. Experimental
results show that the proposed approach significantly improves the verification
and identification performance and the success rate is 99.25%.
Fast L1-Minimization Algorithms For Robust Face Recognition
Authors: Allen Y. Yang, Zihan Zhou, Arvind Ganesh, S. Shankar Sastry, Yi Ma
Date: 2010-07-21 00:00:00
Tasks: Compressive Sensing, Face Recognition, Robust Face Recognition

L1-minimization refers to finding the minimum L1-norm solution to an
underdetermined linear system b=Ax. Under certain conditions as described in
compressive sensing theory, the minimum L1-norm solution is also the sparsest
solution. In this paper, our study addresses the speed and scalability of its
algorithms. In particular, we focus on the numerical implementation of a
sparsity-based classification framework in robust face recognition, where
sparse representation is sought to recover human identities from very
high-dimensional facial images that may be corrupted by illumination, facial
disguise, and pose variation. Although the underlying numerical problem is a
linear program, traditional algorithms are known to suffer poor scalability for
large-scale applications. We investigate a new solution based on a classical
convex optimization framework, known as Augmented Lagrangian Methods (ALM). The
new convex solvers provide a viable solution to real-world, time-critical
applications such as face recognition. We conduct extensive experiments to
validate and compare the performance of the ALM algorithms against several
popular L1-minimization solvers, including interior-point method, Homotopy,
FISTA, SESOP-PCD, approximate message passing (AMP) and TFOCS. To aid peer
evaluation, the code for all the algorithms has been made publicly available.
Weighted Attribute Fusion Model for Face Recognition
Authors: S. Sakthivel, R. Lakshmipathi
Date: 2010-09-03 00:00:00
Tasks: Attribute, Face Recognition, model

Recognizing a face based on its attributes is an easy task for a human to
perform as it is a cognitive process. In recent years, Face Recognition is
achieved with different kinds of facial features which were used separately or
in a combined manner. Currently, Feature fusion methods and parallel methods
are the facial features used and performed by integrating multiple feature sets
at different levels. However, this integration and the combinational methods do
not guarantee better result. Hence to achieve better results, the feature
fusion model with multiple weighted facial attribute set is selected. For this
feature model, face images from predefined data set has been taken from
Olivetti Research Laboratory (ORL) and applied on different methods like
Principal Component Analysis (PCA) based Eigen feature extraction technique,
Discrete Cosine Transformation (DCT) based feature extraction technique,
Histogram Based Feature Extraction technique and Simple Intensity based
features. The extracted feature set obtained from these methods were compared
and tested for accuracy. In this work we have developed a model which will use
the above set of feature extraction techniques with different levels of weights
to attain better accuracy. The results show that the selection of optimum
weight for a particular feature will lead to improvement in recognition rate.
Feature selection via simultaneous sparse approximation for person specific face verification
Authors: Yixiong Liang, Lei Wang, Shenghui Liao, Beiji Zou
Date: 2011-02-14 00:00:00
Tasks: Face Recognition, Face Verification, feature selection

There is an increasing use of some imperceivable and redundant local features
for face recognition. While only a relatively small fraction of them is
relevant to the final recognition task, the feature selection is a crucial and
necessary step to select the most discriminant ones to obtain a compact face
representation. In this paper, we investigate the sparsity-enforced
regularization-based feature selection methods and propose a multi-task feature
selection method for building person specific models for face verification. We
assume that the person specific models share a common subset of features and
novelly reformulated the common subset selection problem as a simultaneous
sparse approximation problem. To the best of our knowledge, it is the first
time to apply the sparsity-enforced regularization methods for person specific
face verification. The effectiveness of the proposed methods is verified with
the challenging LFW face databases.
Feature Selection via Sparse Approximation for Face Recognition
Authors: Yixiong Liang, Lei Wang, Yao Xiang, Beiji Zou
Date: 2011-02-14 00:00:00
Tasks: Face Recognition, feature selection

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.
A Statistical Nonparametric Approach of Face Recognition: Combination of Eigenface & Modified k-Means Clustering
Authors: Soumen Bag, Soumen Barik, Prithwiraj Sen, Gautam Sanyal
Date: 2011-04-07 00:00:00
Tasks: Clustering, Face Recognition

Facial expressions convey non-verbal cues, which play an important role in
interpersonal relations. Automatic recognition of human face based on facial
expression can be an important component of natural human-machine interface. It
may also be used in behavioural science. Although human can recognize the face
practically without any effort, but reliable face recognition by machine is a
challenge. This paper presents a new approach for recognizing the face of a
person considering the expressions of the same human face at different
instances of time. This methodology is developed combining Eigenface method for
feature extraction and modified k-Means clustering for identification of the
human face. This method endowed the face recognition without using the
conventional distance measure classifiers. Simulation results show that
proposed face recognition using perception of k-Means clustering is useful for
face images with different facial expressions.
Face Recognition using 3D Facial Shape and Color Map Information: Comparison and Combination
Authors: Afzal Godil, Sandy Ressler, Patrick Grother
Date: 2011-05-13 00:00:00
Tasks: Face Recognition

In this paper, we investigate the use of 3D surface geometry for face
recognition and compare it to one based on color map information. The 3D
surface and color map data are from the CAESAR anthropometric database. We find
that the recognition performance is not very different between 3D surface and
color map information using a principal component analysis algorithm. We also
discuss the different techniques for the combination of the 3D surface and
color map information for multi-modal recognition by using different fusion
approaches and show that there is significant improvement in results. The
effectiveness of various techniques is compared and evaluated on a dataset with
200 subjects in two different positions.
Next Level of Data Fusion for Human Face Recognition
Authors: Mrinal Kanti Bhowmik, Gautam Majumdar, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri
Date: 2011-06-17 00:00:00
Tasks: Face Recognition

This paper demonstrates two different fusion techniques at two different
levels of a human face recognition process. The first one is called data fusion
at lower level and the second one is the decision fusion towards the end of the
recognition process. At first a data fusion is applied on visual and
corresponding thermal images to generate fused image. Data fusion is
implemented in the wavelet domain after decomposing the images through
Daubechies wavelet coefficients (db2). During the data fusion maximum of
approximate and other three details coefficients are merged together. After
that Principle Component Analysis (PCA) is applied over the fused coefficients
and finally two different artificial neural networks namely Multilayer
Perceptron(MLP) and Radial Basis Function(RBF) networks have been used
separately to classify the images. After that, for decision fusion based
decisions from both the classifiers are combined together using Bayesian
formulation. For experiments, IRIS thermal/visible Face Database has been used.
Experimental results show that the performance of multiple classifier system
along with decision fusion works well over the single classifier system.
High Performance Human Face Recognition using Independent High Intensity Gabor Wavelet Responses: A Statistical Approach
Authors: Arindam Kar, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, Mahantapas Kundu
Date: 2011-06-17 00:00:00
Tasks: Face Recognition, image-classification, Image Classification, Vocal Bursts Intensity Prediction

In this paper, we present a technique by which high-intensity feature vectors
extracted from the Gabor wavelet transformation of frontal face images, is
combined together with Independent Component Analysis (ICA) for enhanced face
recognition. Firstly, the high-intensity feature vectors are automatically
extracted using the local characteristics of each individual face from the
Gabor transformed images. Then ICA is applied on these locally extracted
high-intensity feature vectors of the facial images to obtain the independent
high intensity feature (IHIF) vectors. These IHIF forms the basis of the work.
Finally, the image classification is done using these IHIF vectors, which are
considered as representatives of the images. The importance behind implementing
ICA along with the high-intensity features of Gabor wavelet transformation is
twofold. On the one hand, selecting peaks of the Gabor transformed face images
exhibit strong characteristics of spatial locality, scale, and orientation
selectivity. Thus these images produce salient local features that are most
suitable for face recognition. On the other hand, as the ICA employs locally
salient features from the high informative facial parts, it reduces redundancy
and represents independent features explicitly. These independent features are
most useful for subsequent facial discrimination and associative recall. The
efficiency of IHIF method is demonstrated by the experiment on frontal facial
images dataset, selected from the FERET, FRAV2D, and the ORL database.
Face Identification from Manipulated Facial Images using SIFT
Authors: H. R. Chennamma, Lalitha Rangarajan, Veerabhadrappa
Date: 2011-06-24 00:00:00
Tasks: Face Identification

Editing on digital images is ubiquitous. Identification of deliberately
modified facial images is a new challenge for face identification system. In
this paper, we address the problem of identification of a face or person from
heavily altered facial images. In this face identification problem, the input
to the system is a manipulated or transformed face image and the system reports
back the determined identity from a database of known individuals. Such a
system can be useful in mugshot identification in which mugshot database
contains two views (frontal and profile) of each criminal. We considered only
frontal view from the available database for face identification and the query
image is a manipulated face generated by face transformation software tool
available online. We propose SIFT features for efficient face identification in
this scenario. Further comparative analysis has been given with well known
eigenface approach. Experiments have been conducted with real case images to
evaluate the performance of both methods.
Face Recognition using Curvelet Transform
Authors: Rami Cohen
Date: 2011-07-14 00:00:00
Tasks: Dimensionality Reduction, Face Recognition

Face recognition has been studied extensively for more than 20 years now.
Since the beginning of 90s the subject has became a major issue. This
technology is used in many important real-world applications, such as video
surveillance, smart cards, database security, internet and intranet access.
This report reviews recent two algorithms for face recognition which take
advantage of a relatively new multiscale geometric analysis tool - Curvelet
transform, for facial processing and feature extraction. This transform proves
to be efficient especially due to its good ability to detect curves and lines,
which characterize the human's face. An algorithm which is based on the two
algorithms mentioned above is proposed, and its performance is evaluated on
three data bases of faces: AT&T (ORL), Essex Grimace and Georgia-Tech.
k-nearest neighbour (k-NN) and Support vector machine (SVM) classifiers are
used, along with Principal Component Analysis (PCA) for dimensionality
reduction. This algorithm shows good results, and it even outperforms other
algorithms in some cases.
Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition
Authors: Yaniv Taigman, Lior Wolf
Date: 2011-08-04 00:00:00
Tasks: Face Recognition

We employ the face recognition technology developed in house at face.com to a
well accepted benchmark and show that without any tuning we are able to
considerably surpass state of the art results. Much of the improvement is
concentrated in the high-valued performance point of zero false positive
matches, where the obtained recall rate almost doubles the best reported result
to date. We discuss the various components and innovations of our system that
enable this significant performance gap. These components include extensive
utilization of an accurate 3D reconstructed shape model dealing with challenges
arising from pose and illumination. In addition, discriminative models based on
billions of faces are used in order to overcome aging and facial expression as
well as low light and overexposure. Finally, we identify a challenging set of
identification queries that might provide useful focus for future research.
Real time face recognition using adaboost improved fast PCA algorithm
Authors: K. Susheel Kumar, Vijay Bhaskar Semwal, R C Tripathi
Date: 2011-08-05 00:00:00
Tasks: Face Recognition

This paper presents an automated system for human face recognition in a real
time background world for a large homemade dataset of persons face. The task is
very difficult as the real time background subtraction in an image is still a
challenge. Addition to this there is a huge variation in human face image in
terms of size, pose and expression. The system proposed collapses most of this
variance. To detect real time human face AdaBoost with Haar cascade is used and
a simple fast PCA and LDA is used to recognize the faces detected. The matched
face is then used to mark attendance in the laboratory, in our case. This
biometric system is a real time attendance system based on the human face
recognition with a simple and fast algorithms and gaining a high accuracy
rate..
Face Recognition using Optimal Representation Ensemble
Authors: Hanxi Li, Chunhua Shen, Yongsheng Gao
Date: 2011-10-03 00:00:00
Tasks: Face Recognition, Model Selection

Recently, the face recognizers based on linear representations have been
shown to deliver state-of-the-art performance. In real-world applications,
however, face images usually suffer from expressions, disguises and random
occlusions. The problematic facial parts undermine the validity of the
linear-subspace assumption and thus the recognition performance deteriorates
significantly. In this work, we address the problem in a
learning-inference-mixed fashion. By observing that the linear-subspace
assumption is more reliable on certain face patches rather than on the holistic
face, some Bayesian Patch Representations (BPRs) are randomly generated and
interpreted according to the Bayes' theory. We then train an ensemble model
over the patch-representations by minimizing the empirical risk w.r.t the
"leave-one-out margins". The obtained model is termed Optimal Representation
Ensemble (ORE), since it guarantees the optimality from the perspective of
Empirical Risk Minimization. To handle the unknown patterns in test faces, a
robust version of BPR is proposed by taking the non-face category into
consideration. Equipped with the Robust-BPRs, the inference ability of ORE is
increased dramatically and several record-breaking accuracies (99.9% on Yale-B
and 99.5% on AR) and desirable efficiencies (below 20 ms per face in Matlab)
are achieved. It also overwhelms other modular heuristics on the faces with
random occlusions, extreme expressions and disguises. Furthermore, to
accommodate immense BPRs sets, a boosting-like algorithm is also derived. The
boosted model, a.k.a Boosted-ORE, obtains similar performance to its prototype.
Besides the empirical superiorities, two desirable features of the proposed
methods, namely, the training-determined model-selection and the
data-weight-free boosting procedure, are also theoretically verified.
A Face Recognition Scheme using Wavelet Based Dominant Features
Authors: Hafiz Imtiaz, Shaikh Anowarul Fattah
Date: 2011-10-07 00:00:00
Tasks: Face Recognition

In this paper, a multi-resolution feature extraction algorithm for face
recognition is proposed based on two-dimensional discrete wavelet transform
(2D-DWT), which efficiently exploits the local spatial variations in a face
image. For the purpose of feature extraction, instead of considering the entire
face image, an entropy-based local band selection criterion is developed, which
selects high-informative horizontal segments from the face image. In order to
capture the local spatial variations within these highinformative horizontal
bands precisely, the horizontal band is segmented into several small spatial
modules. Dominant wavelet coefficients corresponding to each local region
residing inside those horizontal bands are selected as features. In the
selection of the dominant coefficients, a threshold criterion is proposed,
which not only drastically reduces the feature dimension but also provides high
within-class compactness and high between-class separability. A principal
component analysis is performed to further reduce the dimensionality of the
feature space. Extensive experimentation is carried out upon standard face
databases and a very high degree of recognition accuracy is achieved by the
proposed method in comparison to those obtained by some of the existing
methods.
Face Recognition Based on SVM and 2DPCA
Authors: Thai Hoang Le, Len Bui
Date: 2011-10-25 00:00:00
Tasks: Classification, Face Recognition, General Classification

The paper will present a novel approach for solving face recognition problem.
Our method combines 2D Principal Component Analysis (2DPCA), one of the
prominent methods for extracting feature vectors, and Support Vector Machine
(SVM), the most powerful discriminative method for classification. Experiments
based on proposed method have been conducted on two public data sets FERET and
AT&T; the results show that the proposed method could improve the
classification rates.
Sparsity and Robustness in Face Recognition
Authors: John Wright, Arvind Ganesh, Allen Yang, Zihan Zhou, Yi Ma
Date: 2011-11-03 00:00:00
Tasks: Face Recognition, Robust Face Recognition

This report concerns the use of techniques for sparse signal representation
and sparse error correction for automatic face recognition. Much of the recent
interest in these techniques comes from the paper "Robust Face Recognition via
Sparse Representation" by Wright et al. (2009), which showed how, under certain
technical conditions, one could cast the face recognition problem as one of
seeking a sparse representation of a given input face image in terms of a
"dictionary" of training images and images of individual pixels. In this
report, we have attempted to clarify some frequently encountered questions
about this work and particularly, on the validity of using sparse
representation techniques for face recognition.