2,415 research outputs found
Shape Primitive Histogram: A Novel Low-Level Face Representation for Face Recognition
We further exploit the representational power of Haar wavelet and present a
novel low-level face representation named Shape Primitives Histogram (SPH) for
face recognition. Since human faces exist abundant shape features, we address
the face representation issue from the perspective of the shape feature
extraction. In our approach, we divide faces into a number of tiny shape
fragments and reduce these shape fragments to several uniform atomic shape
patterns called Shape Primitives. A convolution with Haar Wavelet templates is
applied to each shape fragment to identify its belonging shape primitive. After
that, we do a histogram statistic of shape primitives in each spatial local
image patch for incorporating the spatial information. Finally, each face is
represented as a feature vector via concatenating all the local histograms of
shape primitives. Four popular face databases, namely ORL, AR, Yale-B and LFW-a
databases, are employed to evaluate SPH and experimentally study the choices of
the parameters. Extensive experimental results demonstrate that the proposed
approach outperform the state-of-the-arts.Comment: second version, two columns and 11 page
Face Recognition: From Traditional to Deep Learning Methods
Starting in the seventies, face recognition has become one of the most
researched topics in computer vision and biometrics. Traditional methods based
on hand-crafted features and traditional machine learning techniques have
recently been superseded by deep neural networks trained with very large
datasets. In this paper we provide a comprehensive and up-to-date literature
review of popular face recognition methods including both traditional
(geometry-based, holistic, feature-based and hybrid methods) and deep learning
methods
Automatic Facial Expression Recognition Using Features of Salient Facial Patches
Extraction of discriminative features from salient facial patches plays a
vital role in effective facial expression recognition. The accurate detection
of facial landmarks improves the localization of the salient patches on face
images. This paper proposes a novel framework for expression recognition by
using appearance features of selected facial patches. A few prominent facial
patches, depending on the position of facial landmarks, are extracted which are
active during emotion elicitation. These active patches are further processed
to obtain the salient patches which contain discriminative features for
classification of each pair of expressions, thereby selecting different facial
patches as salient for different pair of expression classes. One-against-one
classification method is adopted using these features. In addition, an
automated learning-free facial landmark detection technique has been proposed,
which achieves similar performances as that of other state-of-art landmark
detection methods, yet requires significantly less execution time. The proposed
method is found to perform well consistently in different resolutions, hence,
providing a solution for expression recognition in low resolution images.
Experiments on CK+ and JAFFE facial expression databases show the effectiveness
of the proposed system
Illumination Normalization via Merging Locally Enhanced Textures for Robust Face Recognition
In order to improve the accuracy of face recognition under varying
illumination conditions, a local texture enhanced illumination normalization
method based on fusion of differential filtering images (FDFI-LTEIN) is
proposed to weaken the influence caused by illumination changes. Firstly, the
dynamic range of the face image in dark or shadowed regions is expanded by
logarithmic transformation. Then, the global contrast enhanced face image is
convolved with difference of Gaussian filters and difference of bilateral
filters, and the filtered images are weighted and merged using a coefficient
selection rule based on the standard deviation (SD) of image, which can enhance
image texture information while filtering out most noise. Finally, the local
contrast equalization (LCE) is performed on the fused face image to reduce the
influence caused by over or under saturated pixel values in highlight or dark
regions. Experimental results on the Extended Yale B face database and CMU PIE
face database demonstrate that the proposed method is more robust to
illumination changes and achieve higher recognition accuracy when compared with
other illumination normalization methods and a deep CNNs based illumination
invariant face recognition methodComment: 10 page
Real time face recognition using adaboost improved fast PCA algorithm
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..Comment: 14 pages; ISSN : 0975-900X (Online), 0976-2191 (Print
PCANet: A Simple Deep Learning Baseline for Image Classification?
In this work, we propose a very simple deep learning network for image
classification which comprises only the very basic data processing components:
cascaded principal component analysis (PCA), binary hashing, and block-wise
histograms. In the proposed architecture, PCA is employed to learn multistage
filter banks. It is followed by simple binary hashing and block histograms for
indexing and pooling. This architecture is thus named as a PCA network (PCANet)
and can be designed and learned extremely easily and efficiently. For
comparison and better understanding, we also introduce and study two simple
variations to the PCANet, namely the RandNet and LDANet. They share the same
topology of PCANet but their cascaded filters are either selected randomly or
learned from LDA. We have tested these basic networks extensively on many
benchmark visual datasets for different tasks, such as LFW for face
verification, MultiPIE, Extended Yale B, AR, FERET datasets for face
recognition, as well as MNIST for hand-written digits recognition.
Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with
the state of the art features, either prefixed, highly hand-crafted or
carefully learned (by DNNs). Even more surprisingly, it sets new records for
many classification tasks in Extended Yale B, AR, FERET datasets, and MNIST
variations. Additional experiments on other public datasets also demonstrate
the potential of the PCANet serving as a simple but highly competitive baseline
for texture classification and object recognition
The Indian Spontaneous Expression Database for Emotion Recognition
Automatic recognition of spontaneous facial expressions is a major challenge
in the field of affective computing. Head rotation, face pose, illumination
variation, occlusion etc. are the attributes that increase the complexity of
recognition of spontaneous expressions in practical applications. Effective
recognition of expressions depends significantly on the quality of the database
used. Most well-known facial expression databases consist of posed expressions.
However, currently there is a huge demand for spontaneous expression databases
for the pragmatic implementation of the facial expression recognition
algorithms. In this paper, we propose and establish a new facial expression
database containing spontaneous expressions of both male and female
participants of Indian origin. The database consists of 428 segmented video
clips of the spontaneous facial expressions of 50 participants. In our
experiment, emotions were induced among the participants by using emotional
videos and simultaneously their self-ratings were collected for each
experienced emotion. Facial expression clips were annotated carefully by four
trained decoders, which were further validated by the nature of stimuli used
and self-report of emotions. An extensive analysis was carried out on the
database using several machine learning algorithms and the results are provided
for future reference. Such a spontaneous database will help in the development
and validation of algorithms for recognition of spontaneous expressions.Comment: in IEEE Transactions on Affective Computing, 201
The Cross-Depiction Problem: Computer Vision Algorithms for Recognising Objects in Artwork and in Photographs
The cross-depiction problem is that of recognising visual objects regardless
of whether they are photographed, painted, drawn, etc. It is a potentially
significant yet under-researched problem. Emulating the remarkable human
ability to recognise objects in an astonishingly wide variety of depictive
forms is likely to advance both the foundations and the applications of
Computer Vision.
In this paper we benchmark classification, domain adaptation, and deep
learning methods; demonstrating that none perform consistently well in the
cross-depiction problem. Given the current interest in deep learning, the fact
such methods exhibit the same behaviour as all but one other method: they show
a significant fall in performance over inhomogeneous databases compared to
their peak performance, which is always over data comprising photographs only.
Rather, we find the methods that have strong models of spatial relations
between parts tend to be more robust and therefore conclude that such
information is important in modelling object classes regardless of appearance
details.Comment: 12 pages, 6 figure
Machine Learning Techniques and Applications For Ground-based Image Analysis
Ground-based whole sky cameras have opened up new opportunities for
monitoring the earth's atmosphere. These cameras are an important complement to
satellite images by providing geoscientists with cheaper, faster, and more
localized data. The images captured by whole sky imagers can have high spatial
and temporal resolution, which is an important pre-requisite for applications
such as solar energy modeling, cloud attenuation analysis, local weather
prediction, etc.
Extracting valuable information from the huge amount of image data by
detecting and analyzing the various entities in these images is challenging.
However, powerful machine learning techniques have become available to aid with
the image analysis. This article provides a detailed walk-through of recent
developments in these techniques and their applications in ground-based
imaging. We aim to bridge the gap between computer vision and remote sensing
with the help of illustrative examples. We demonstrate the advantages of using
machine learning techniques in ground-based image analysis via three primary
applications -- segmentation, classification, and denoising
Vision-based Human Gender Recognition: A Survey
Gender is an important demographic attribute of people. This paper provides a
survey of human gender recognition in computer vision. A review of approaches
exploiting information from face and whole body (either from a still image or
gait sequence) is presented. We highlight the challenges faced and survey the
representative methods of these approaches. Based on the results, good
performance have been achieved for datasets captured under controlled
environments, but there is still much work that can be done to improve the
robustness of gender recognition under real-life environments.Comment: 30 page
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