24,873 research outputs found
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
Using Deep Autoencoders for Facial Expression Recognition
Feature descriptors involved in image processing are generally manually
chosen and high dimensional in nature. Selecting the most important features is
a very crucial task for systems like facial expression recognition. This paper
investigates the performance of deep autoencoders for feature selection and
dimension reduction for facial expression recognition on multiple levels of
hidden layers. The features extracted from the stacked autoencoder outperformed
when compared to other state-of-the-art feature selection and dimension
reduction techniques
Human Emotional Facial Expression Recognition
An automatic Facial Expression Recognition (FER) model with Adaboost face
detector, feature selection based on manifold learning and synergetic prototype
based classifier has been proposed. Improved feature selection method and
proposed classifier can achieve favorable effectiveness to performance FER in
reasonable processing time
Facial expression recognition based on local region specific features and support vector machines
Facial expressions are one of the most powerful, natural and immediate means
for human being to communicate their emotions and intensions. Recognition of
facial expression has many applications including human-computer interaction,
cognitive science, human emotion analysis, personality development etc. In this
paper, we propose a new method for the recognition of facial expressions from
single image frame that uses combination of appearance and geometric features
with support vector machines classification. In general, appearance features
for the recognition of facial expressions are computed by dividing face region
into regular grid (holistic representation). But, in this paper we extracted
region specific appearance features by dividing the whole face region into
domain specific local regions. Geometric features are also extracted from
corresponding domain specific regions. In addition, important local regions are
determined by using incremental search approach which results in the reduction
of feature dimension and improvement in recognition accuracy. The results of
facial expressions recognition using features from domain specific regions are
also compared with the results obtained using holistic representation. The
performance of the proposed facial expression recognition system has been
validated on publicly available extended Cohn-Kanade (CK+) facial expression
data sets.Comment: Facial expressions, Local representation, Appearance features,
Geometric features, Support vector machine
An adaptive block based integrated LDP,GLCM,and Morphological features for Face Recognition
This paper proposes a technique for automatic face recognition using
integrated multiple feature sets extracted from the significant blocks of a
gradient image. We discuss about the use of novel morphological, local
directional pattern (LDP) and gray-level co-occurrence matrix GLCM based
feature extraction technique to recognize human faces. Firstly, the new
morphological features i.e., features based on number of runs of pixels in four
directions (N,NE,E,NW) are extracted, together with the GLCM based statistical
features and LDP features that are less sensitive to the noise and
non-monotonic illumination changes, are extracted from the significant blocks
of the gradient image. Then these features are concatenated together. We
integrate the above mentioned methods to take full advantage of the three
approaches. Extraction of the significant blocks from the absolute gradient
image and hence from the original image to extract pertinent information with
the idea of dimension reduction forms the basis of the work. The efficiency of
our method is demonstrated by the experiment on 1100 images from the FRAV2D
face database, 2200 images from the FERET database, where the images vary in
pose, expression, illumination and scale and 400 images from the ORL face
database, where the images slightly vary in pose. Our method has shown 90.3%,
93% and 98.75% recognition accuracy for the FRAV2D, FERET and the ORL database
respectively.Comment: 7 pages, Science Academy Publisher, United Kingdo
Soft Locality Preserving Map (SLPM) for Facial Expression Recognition
For image recognition, an extensive number of methods have been proposed to
overcome the high-dimensionality problem of feature vectors being used. These
methods vary from unsupervised to supervised, and from statistics to
graph-theory based. In this paper, the most popular and the state-of-the-art
methods for dimensionality reduction are firstly reviewed, and then a new and
more efficient manifold-learning method, named Soft Locality Preserving Map
(SLPM), is presented. Furthermore, feature generation and sample selection are
proposed to achieve better manifold learning. SLPM is a graph-based
subspace-learning method, with the use of k-neighbourhood information and the
class information. The key feature of SLPM is that it aims to control the level
of spread of the different classes, because the spread of the classes in the
underlying manifold is closely connected to the generalizability of the learned
subspace. Our proposed manifold-learning method can be applied to various
pattern recognition applications, and we evaluate its performances on facial
expression recognition. Experiments on databases, such as the Bahcesehir
University Multilingual Affective Face Database (BAUM-2), the Extended
Cohn-Kanade (CK+) Database, the Japanese Female Facial Expression (JAFFE)
Database, and the Taiwanese Facial Expression Image Database (TFEID), show that
SLPM can effectively reduce the dimensionality of the feature vectors and
enhance the discriminative power of the extracted features for expression
recognition. Furthermore, the proposed feature-generation method can improve
the generalizability of the underlying manifolds for facial expression
recognition
A Facial Affect Analysis System for Autism Spectrum Disorder
In this paper, we introduce an end-to-end machine learning-based system for
classifying autism spectrum disorder (ASD) using facial attributes such as
expressions, action units, arousal, and valence. Our system classifies ASD
using representations of different facial attributes from convolutional neural
networks, which are trained on images in the wild. Our experimental results
show that different facial attributes used in our system are statistically
significant and improve sensitivity, specificity, and F1 score of ASD
classification by a large margin. In particular, the addition of different
facial attributes improves the performance of ASD classification by about 7%
which achieves a F1 score of 76%.Comment: 5 pages (including 1 page for reference), 3 figure
Facial Expression Detection using Patch-based Eigen-face Isomap Networks
Automated facial expression detection problem pose two primary challenges
that include variations in expression and facial occlusions (glasses, beard,
mustache or face covers). In this paper we introduce a novel automated patch
creation technique that masks a particular region of interest in the face,
followed by Eigen-value decomposition of the patched faces and generation of
Isomaps to detect underlying clustering patterns among faces. The proposed
masked Eigen-face based Isomap clustering technique achieves 75% sensitivity
and 66-73% accuracy in classification of faces with occlusions and smiling
faces in around 1 second per image. Also, betweenness centrality, Eigen
centrality and maximum information flow can be used as network-based measures
to identify the most significant training faces for expression classification
tasks. The proposed method can be used in combination with feature-based
expression classification methods in large data sets for improving expression
classification accuracies.Comment: 6 pages,7 figures, IJCAI-HINA 201
A Survey of the Trends in Facial and Expression Recognition Databases and Methods
Automated facial identification and facial expression recognition have been
topics of active research over the past few decades. Facial and expression
recognition find applications in human-computer interfaces, subject tracking,
real-time security surveillance systems and social networking. Several holistic
and geometric methods have been developed to identify faces and expressions
using public and local facial image databases. In this work we present the
evolution in facial image data sets and the methodologies for facial
identification and recognition of expressions such as anger, sadness,
happiness, disgust, fear and surprise. We observe that most of the earlier
methods for facial and expression recognition aimed at improving the
recognition rates for facial feature-based methods using static images.
However, the recent methodologies have shifted focus towards robust
implementation of facial/expression recognition from large image databases that
vary with space (gathered from the internet) and time (video recordings). The
evolution trends in databases and methodologies for facial and expression
recognition can be useful for assessing the next-generation topics that may
have applications in security systems or personal identification systems that
involve "Quantitative face" assessments.Comment: 16 pages, 4 figures, 3 tables, International Journal of Computer
Science and Engineering Survey, October, 201
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Automated affective computing in the wild setting is a challenging problem in
computer vision. Existing annotated databases of facial expressions in the wild
are small and mostly cover discrete emotions (aka the categorical model). There
are very limited annotated facial databases for affective computing in the
continuous dimensional model (e.g., valence and arousal). To meet this need, we
collected, annotated, and prepared for public distribution a new database of
facial emotions in the wild (called AffectNet). AffectNet contains more than
1,000,000 facial images from the Internet by querying three major search
engines using 1250 emotion related keywords in six different languages. About
half of the retrieved images were manually annotated for the presence of seven
discrete facial expressions and the intensity of valence and arousal. AffectNet
is by far the largest database of facial expression, valence, and arousal in
the wild enabling research in automated facial expression recognition in two
different emotion models. Two baseline deep neural networks are used to
classify images in the categorical model and predict the intensity of valence
and arousal. Various evaluation metrics show that our deep neural network
baselines can perform better than conventional machine learning methods and
off-the-shelf facial expression recognition systems.Comment: IEEE Transactions on Affective Computing, 201
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