pwc-archive/papers-with-abstracts huggingface dataset:
Image-based Face Detection and Recognition: "State of the Art"
Authors: Faizan Ahmad, Aaima Najam, Zeeshan Ahmed
Date: 2013-02-26 00:00:00
Tasks: Face Detection, Face Recognition

Face recognition from image or video is a popular topic in biometrics
research. Many public places usually have surveillance cameras for video
capture and these cameras have their significant value for security purpose. It
is widely acknowledged that the face recognition have played an important role
in surveillance system as it doesn't need the object's cooperation. The actual
advantages of face based identification over other biometrics are uniqueness
and acceptance. As human face is a dynamic object having high degree of
variability in its appearance, that makes face detection a difficult problem in
computer vision. In this field, accuracy and speed of identification is a main
issue.
  The goal of this paper is to evaluate various face detection and recognition
methods, provide complete solution for image based face detection and
recognition with higher accuracy, better response rate as an initial step for
video surveillance. Solution is proposed based on performed tests on various
face rich databases in terms of subjects, pose, emotions, race and light.
Face Alignment Using Active Shape Model And Support Vector Machine
Authors: Thai Hoang Le, Truong Nhat Vo
Date: 2012-09-27 00:00:00
Tasks: Face Alignment, Face Generation

The Active Shape Model (ASM) is one of the most popular local texture models
for face alignment. It applies in many fields such as locating facial features
in the image, face synthesis, etc. However, the experimental results show that
the accuracy of the classical ASM for some applications is not high. This paper
suggests some improvements on the classical ASM to increase the performance of
the model in the application: face alignment. Four of our major improvements
include: i) building a model combining Sobel filter and the 2-D profile in
searching face in image; ii) applying Canny algorithm for the enhancement edge
on image; iii) Support Vector Machine (SVM) is used to classify landmarks on
face, in order to determine exactly location of these landmarks support for
ASM; iv)automatically adjust 2-D profile in the multi-level model based on the
size of the input image. The experimental results on Caltech face database and
Technical University of Denmark database (imm_face) show that our proposed
improvement leads to far better performance.
3D Face Recognition with Sparse Spherical Representations
Authors: R. Sala Llonch, E. Kokiopoulou, I. Tosic, P. Frossard
Date: 2008-10-29 00:00:00
Tasks: Dimensionality Reduction, Face Recognition

This paper addresses the problem of 3D face recognition using simultaneous
sparse approximations on the sphere. The 3D face point clouds are first aligned
with a novel and fully automated registration process. They are then
represented as signals on the 2D sphere in order to preserve depth and geometry
information. Next, we implement a dimensionality reduction process with
simultaneous sparse approximations and subspace projection. It permits to
represent each 3D face by only a few spherical functions that are able to
capture the salient facial characteristics, and hence to preserve the
discriminant facial information. We eventually perform recognition by effective
matching in the reduced space, where Linear Discriminant Analysis can be
further activated for improved recognition performance. The 3D face recognition
algorithm is evaluated on the FRGC v.1.0 data set, where it is shown to
outperform classical state-of-the-art solutions that work with depth images.
Feature Selection By KDDA For SVM-Based MultiView Face Recognition
Authors: Seyyed Majid Valiollahzadeh, Abolghasem Sayadiyan, Mohammad Nazari
Date: 2008-12-13 00:00:00
Tasks: Classification, Face Recognition, feature selection, General Classification

Applications such as face recognition that deal with high-dimensional data
need a mapping technique that introduces representation of low-dimensional
features with enhanced discriminatory power and a proper classifier, able to
classify those complex features. Most of traditional Linear Discriminant
Analysis suffer from the disadvantage that their optimality criteria are not
directly related to the classification ability of the obtained feature
representation. Moreover, their classification accuracy is affected by the
"small sample size" problem which is often encountered in FR tasks. In this
short paper, we combine nonlinear kernel based mapping of data called KDDA with
Support Vector machine classifier to deal with both of the shortcomings in an
efficient and cost effective manner. The proposed here method is compared, in
terms of classification accuracy, to other commonly used FR methods on UMIST
face database. Results indicate that the performance of the proposed method is
overall superior to those of traditional FR approaches, such as the Eigenfaces,
Fisherfaces, and D-LDA methods and traditional linear classifiers.
Parallel AdaBoost Algorithm for Gabor Wavelet Selection in Face Recognition
Authors: Ulas Bagci, Li Bai
Date: 2009-07-18 00:00:00
Tasks: Classification, Face Recognition, General Classification

In this paper, the problem of automatic Gabor wavelet selection for face
recognition is tackled by introducing an automatic algorithm based on Parallel
AdaBoosting method. Incorporating mutual information into the algorithm leads
to the selection procedure not only based on classification accuracy but also
on efficiency. Effective image features are selected by using properly chosen
Gabor wavelets optimised with Parallel AdaBoost method and mutual information
to get high recognition rates with low computational cost. Experiments are
conducted using the well-known FERET face database. In proposed framework,
memory and computation costs are reduced significantly and high classification
accuracy is obtained.
Automatic local Gabor Features extraction for face recognition
Authors: Yousra Ben Jemaa, Sana Khanfir
Date: 2009-07-28 00:00:00
Tasks: Face Detection, Face Recognition

We present in this paper a biometric system of face detection and recognition
in color images. The face detection technique is based on skin color
information and fuzzy classification. A new algorithm is proposed in order to
detect automatically face features (eyes, mouth and nose) and extract their
correspondent geometrical points. These fiducial points are described by sets
of wavelet components which are used for recognition. To achieve the face
recognition, we use neural networks and we study its performances for different
inputs. We compare the two types of features used for recognition: geometric
distances and Gabor coefficients which can be used either independently or
jointly. This comparison shows that Gabor coefficients are more powerful than
geometric distances. We show with experimental results how the importance
recognition ratio makes our system an effective tool for automatic face
detection and recognition.
SVM-based Multiview Face Recognition by Generalization of Discriminant Analysis
Authors: Dakshina Ranjan Kisku, Hunny Mehrotra, Jamuna Kanta Sing, Phalguni Gupta
Date: 2010-01-23 00:00:00
Tasks: Face Recognition

Identity verification of authentic persons by their multiview faces is a real
valued problem in machine vision. Multiview faces are having difficulties due
to non-linear representation in the feature space. This paper illustrates the
usability of the generalization of LDA in the form of canonical covariate for
face recognition to multiview faces. In the proposed work, the Gabor filter
bank is used to extract facial features that characterized by spatial
frequency, spatial locality and orientation. Gabor face representation captures
substantial amount of variations of the face instances that often occurs due to
illumination, pose and facial expression changes. Convolution of Gabor filter
bank to face images of rotated profile views produce Gabor faces with high
dimensional features vectors. Canonical covariate is then used to Gabor faces
to reduce the high dimensional feature spaces into low dimensional subspaces.
Finally, support vector machines are trained with canonical sub-spaces that
contain reduced set of features and perform recognition task. The proposed
system is evaluated with UMIST face database. The experiment results
demonstrate the efficiency and robustness of the proposed system with high
recognition rates.
Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm
Authors: Dakshina Ranjan Kisku, Massimo Tistarelli, Jamuna Kanta Sing, Phalguni Gupta
Date: 2010-02-02 00:00:00
Tasks: Face Recognition, Robust Face Recognition

Faces are highly deformable objects which may easily change their appearance
over time. Not all face areas are subject to the same variability. Therefore
decoupling the information from independent areas of the face is of paramount
importance to improve the robustness of any face recognition technique. This
paper presents a robust face recognition technique based on the extraction and
matching of SIFT features related to independent face areas. Both a global and
local (as recognition from parts) matching strategy is proposed. The local
strategy is based on matching individual salient facial SIFT features as
connected to facial landmarks such as the eyes and the mouth. As for the global
matching strategy, all SIFT features are combined together to form a single
feature. In order to reduce the identification errors, the Dempster-Shafer
decision theory is applied to fuse the two matching techniques. The proposed
algorithms are evaluated with the ORL and the IITK face databases. The
experimental results demonstrate the effectiveness and potential of the
proposed face recognition techniques also in the case of partially occluded
faces or with missing information.
Face Identification by SIFT-based Complete Graph Topology
Authors: Dakshina Ranjan Kisku, Ajita Rattani, Enrico Grosso, Massimo Tistarelli
Date: 2010-02-02 00:00:00
Tasks: Face Identification, Face Recognition, Graph Matching, object-detection, Object Detection, Translation

This paper presents a new face identification system based on Graph Matching
Technique on SIFT features extracted from face images. Although SIFT features
have been successfully used for general object detection and recognition, only
recently they were applied to face recognition. This paper further investigates
the performance of identification techniques based on Graph matching topology
drawn on SIFT features which are invariant to rotation, scaling and
translation. Face projections on images, represented by a graph, can be matched
onto new images by maximizing a similarity function taking into account spatial
distortions and the similarities of the local features. Two graph based
matching techniques have been investigated to deal with false pair assignment
and reducing the number of features to find the optimal feature set between
database and query face SIFT features. The experimental results, performed on
the BANCA database, demonstrate the effectiveness of the proposed system for
automatic face identification.
Robust multi-camera view face recognition
Authors: Dakshina Ranjan Kisku, Hunny Mehrotra, Phalguni Gupta, Jamuna Kanta Sing
Date: 2010-03-30 00:00:00
Tasks: Face Recognition

This paper presents multi-appearance fusion of Principal Component Analysis
(PCA) and generalization of Linear Discriminant Analysis (LDA) for multi-camera
view offline face recognition (verification) system. The generalization of LDA
has been extended to establish correlations between the face classes in the
transformed representation and this is called canonical covariate. The proposed
system uses Gabor filter banks for characterization of facial features by
spatial frequency, spatial locality and orientation to make compensate to the
variations of face instances occurred due to illumination, pose and facial
expression changes. Convolution of Gabor filter bank to face images produces
Gabor face representations with high dimensional feature vectors. PCA and
canonical covariate are then applied on the Gabor face representations to
reduce the high dimensional feature spaces into low dimensional Gabor
eigenfaces and Gabor canonical faces. Reduced eigenface vector and canonical
face vector are fused together using weighted mean fusion rule. Finally,
support vector machines (SVM) have trained with augmented fused set of features
and perform the recognition task. The system has been evaluated with UMIST face
database consisting of multiview faces. The experimental results demonstrate
the efficiency and robustness of the proposed system for multi-view face images
with high recognition rates. Complexity analysis of the proposed system is also
presented at the end of the experimental results.
Extended Two-Dimensional PCA for Efficient Face Representation and Recognition
Authors: Mehran Safayani, Mohammad T. Manzuri-Shalmani, Mahmoud Khademi
Date: 2010-04-06 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper a novel method called Extended Two-Dimensional PCA (E2DPCA) is
proposed which is an extension to the original 2DPCA. We state that the
covariance matrix of 2DPCA is equivalent to the average of the main diagonal of
the covariance matrix of PCA. This implies that 2DPCA eliminates some
covariance information that can be useful for recognition. E2DPCA instead of
just using the main diagonal considers a radius of r diagonals around it and
expands the averaging so as to include the covariance information within those
diagonals. The parameter r unifies PCA and 2DPCA. r = 1 produces the covariance
of 2DPCA, r = n that of PCA. Hence, by controlling r it is possible to control
the trade-offs between recognition accuracy and energy compression (fewer
coefficients), and between training and recognition complexity. Experiments on
ORL face database show improvement in both recognition accuracy and recognition
time over the original 2DPCA.
Maximized Posteriori Attributes Selection from Facial Salient Landmarks for Face Recognition
Authors: Phalguni Gupta, Dakshina Ranjan Kisku, Jamuna Kanta Sing, Massimo Tistarelli
Date: 2010-04-12 00:00:00
Tasks: Face Recognition

This paper presents a robust and dynamic face recognition technique based on
the extraction and matching of devised probabilistic graphs drawn on SIFT
features related to independent face areas. The face matching strategy is based
on matching individual salient facial graph characterized by SIFT features as
connected to facial landmarks such as the eyes and the mouth. In order to
reduce the face matching errors, the Dempster-Shafer decision theory is applied
to fuse the individual matching scores obtained from each pair of salient
facial features. The proposed algorithm is evaluated with the ORL and the IITK
face databases. The experimental results demonstrate the effectiveness and
potential of the proposed face recognition technique also in case of partially
occluded faces.
Face Synthesis (FASY) System for Generation of a Face Image from Human Description
Authors: Santanu Halder, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, Mahantapas Kundu
Date: 2010-05-21 00:00:00
Tasks: Face Generation, Retrieval

This paper aims at generating a new face based on the human like description
using a new concept. The FASY (FAce SYnthesis) System is a Face Database
Retrieval and new Face generation System that is under development. One of its
main features is the generation of the requested face when it is not found in
the existing database, which allows a continuous growing of the database also.
Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition
Authors: Mrinal Kanti Bhowmik, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, Mahantapas Kundu
Date: 2010-05-21 00:00:00
Tasks: Face Recognition, General Classification, Object Tracking

This paper presents a novel approach to handle the challenges of face
recognition. In this work thermal face images are considered, which minimizes
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. Polar images are projected
into eigenspace and finally classified using a multi-layer perceptron. In the
experiments we have used Object Tracking and Classification Beyond Visible
Spectrum (OTCBVS) database benchmark thermal face images. Experimental results
show that the proposed approach significantly improves the verification and
identification performance and the success rate is 97.05%.
Reduction of Feature Vectors Using Rough Set Theory for Human Face Recognition
Authors: Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, M. Kundu
Date: 2010-05-21 00:00:00
Tasks: Face Recognition, feature selection

In this paper we describe a procedure to reduce the size of the input feature
vector. A complex pattern recognition problem like face recognition involves
huge dimension of input feature vector. To reduce that dimension here we have
used eigenspace projection (also called as Principal Component Analysis), which
is basically transformation of space. To reduce further we have applied feature
selection method to select indispensable features, which will remain in the
final feature vectors. Features those are not selected are removed from the
final feature vector considering them as redundant or superfluous. For
selection of features we have used the concept of reduct and core from rough
set theory. This method has shown very good performance. It is worth to mention
that in some cases the recognition rate increases with the decrease in the
feature vector dimension.
Face Synthesis (FASY) System for Determining the Characteristics of a Face Image
Authors: Santanu Halder, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, Mahantapas Kundu
Date: 2010-07-05 00:00:00
Tasks: Face Generation, Retrieval

This paper aims at determining the characteristics of a face image by
extracting its components. The FASY (FAce SYnthesis) System is a Face Database
Retrieval and new Face generation System that is under development. One of its
main features is the generation of the requested face when it is not found in
the existing database, which allows a continuous growing of the database also.
To generate the new face image, we need to store the face components in the
database. So we have designed a new technique to extract the face components by
a sophisticated method. After extraction of the facial feature points we have
analyzed the components to determine their characteristics. After extraction
and analysis we have stored the components along with their characteristics
into the face database for later use during the face construction.
Quotient Based Multiresolution Image Fusion of Thermal and Visual Images Using Daubechies Wavelet Transform 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: Dimensionality Reduction, Face Recognition

This paper investigates the multiresolution level-1 and level-2 Quotient
based Fusion of thermal and visual images. In the proposed system, the method-1
namely "Decompose then Quotient Fuse Level-1" and the method-2 namely
"Decompose-Reconstruct then Quotient Fuse Level-2" both work on wavelet
transformations of the visual and thermal face images. The wavelet transform is
well-suited to manage different image resolution and allows the image
decomposition in different kinds of coefficients, while preserving the image
information without any loss. This approach is based on a definition of an
illumination invariant signature image which enables an analytic generation of
the image space with varying illumination. The quotient fused images are passed
through Principal Component Analysis (PCA) for dimension reduction and then
those images are classified using a multi-layer perceptron (MLP). The
performances of both the methods have been evaluated using OTCBVS and IRIS
databases. All the different classes have been tested separately, among them
the maximum recognition result is 100%.
Fusion of Daubechies Wavelet Coefficients 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

In this paper fusion of visual and thermal images in wavelet transformed
domain has been presented. Here, Daubechies wavelet transform, called as D2,
coefficients from visual and corresponding coefficients computed in the same
manner from thermal images are combined to get fused coefficients. After
decomposition up to fifth level (Level 5) fusion of coefficients is done.
Inverse Daubechies wavelet transform of those coefficients gives us fused face
images. The main advantage of using wavelet transform is that it is well-suited
to manage different image resolution and allows the image decomposition in
different kinds of coefficients, while preserving the image information. Fused
images thus found are passed through Principal Component Analysis (PCA) for
reduction of dimensions and then those reduced fused images are classified
using a multi-layer perceptron. For experiments IRIS Thermal/Visual Face
Database was used. Experimental results show that the performance of the
approach presented here achieves maximum success rate of 100% in many cases.
Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human Face Recognition - A Comparative Study
Authors: M. K. Bhowmik, Debotosh Bhattacharjee, M. Nasipuri, D. K. Basu, M. Kundu
Date: 2010-07-05 00:00:00
Tasks: Face Recognition

In this paper we present a comparative study on fusion of visual and thermal
images using different wavelet transformations. Here, coefficients of discrete
wavelet transforms from both visual and thermal images are computed separately
and combined. Next, inverse discrete wavelet transformation is taken in order
to obtain fused face image. Both Haar and Daubechies (db2) wavelet transforms
have been used to compare recognition results. For experiments IRIS
Thermal/Visual Face Database was used. Experimental results using Haar and
Daubechies wavelets show that the performance of the approach presented here
achieves maximum success rate of 100% in many cases.
A Parallel Framework for Multilayer Perceptron 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

Artificial neural networks have already shown their success in face
recognition and similar complex pattern recognition tasks. However, a major
disadvantage of the technique is that it is extremely slow during training for
larger classes and hence not suitable for real-time complex problems such as
pattern recognition. This is an attempt to develop a parallel framework for the
training algorithm of a perceptron. In this paper, two general architectures
for a Multilayer Perceptron (MLP) have been demonstrated. The first
architecture is All-Class-in-One-Network (ACON) where all the classes are
placed in a single network and the second one is One-Class-in-One-Network
(OCON) where an individual single network is responsible for each and every
class. Capabilities of these two architectures were compared and verified in
solving human face recognition, which is a complex pattern recognition task
where several factors affect the recognition performance like pose variations,
facial expression changes, occlusions, and most importantly illumination
changes. Both the structures were implemented and tested for face recognition
purpose and experimental results show that the OCON structure performs better
than the generally used ACON ones in term of training convergence speed of the
network. Unlike the conventional sequential approach of training the neural
networks, the OCON technique may be implemented by training all the classes of
the face images simultaneously.