4,541 research outputs found
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy
This work investigates how the traditional image classification pipelines can
be extended into a deep architecture, inspired by recent successes of deep
neural networks. We propose a deep boosting framework based on layer-by-layer
joint feature boosting and dictionary learning. In each layer, we construct a
dictionary of filters by combining the filters from the lower layer, and
iteratively optimize the image representation with a joint
discriminative-generative formulation, i.e. minimization of empirical
classification error plus regularization of analysis image generation over
training images. For optimization, we perform two iterating steps: i) to
minimize the classification error, select the most discriminative features
using the gentle adaboost algorithm; ii) according to the feature selection,
update the filters to minimize the regularization on analysis image
representation using the gradient descent method. Once the optimization is
converged, we learn the higher layer representation in the same way. Our model
delivers several distinct advantages. First, our layer-wise optimization
provides the potential to build very deep architectures. Second, the generated
image representation is compact and meaningful. In several visual recognition
tasks, our framework outperforms existing state-of-the-art approaches
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
A Review on Facial Micro-Expressions Analysis: Datasets, Features and Metrics
Facial micro-expressions are very brief, spontaneous facial expressions that
appear on the face of humans when they either deliberately or unconsciously
conceal an emotion. Micro-expression has shorter duration than
macro-expression, which makes it more challenging for human and machine. Over
the past ten years, automatic micro-expressions recognition has attracted
increasing attention from researchers in psychology, computer science,
security, neuroscience and other related disciplines. The aim of this paper is
to provide the insights of automatic micro-expressions and recommendations for
future research. There has been a lot of datasets released over the last decade
that facilitated the rapid growth in this field. However, comparison across
different datasets is difficult due to the inconsistency in experiment
protocol, features used and evaluation methods. To address these issues, we
review the datasets, features and the performance metrics deployed in the
literature. Relevant challenges such as the spatial temporal settings during
data collection, emotional classes versus objective classes in data labelling,
face regions in data analysis, standardisation of metrics and the requirements
for real-world implementation are discussed. We conclude by proposing some
promising future directions to advancing micro-expressions research.Comment: Preprint submitted to IEEE Transaction
A Vision System for Multi-View Face Recognition
Multimodal biometric identification has been grown a great attention in the
most interests in the security fields. In the real world there exist modern
system devices that are able to detect, recognize, and classify the human
identities with reliable and fast recognition rates. Unfortunately most of
these systems rely on one modality, and the reliability for two or more
modalities are further decreased. The variations of face images with respect to
different poses are considered as one of the important challenges in face
recognition systems. In this paper, we propose a multimodal biometric system
that able to detect the human face images that are not only one view face
image, but also multi-view face images. Each subject entered to the system
adjusted their face at front of the three cameras, and then the features of the
face images are extracted based on Speeded Up Robust Features (SURF) algorithm.
We utilize Multi-Layer Perceptron (MLP) and combined classifiers based on both
Learning Vector Quantization (LVQ), and Radial Basis Function (RBF) for
classification purposes. The proposed system has been tested using SDUMLA-HMT,
and CASIA datasets. Furthermore, we collected a database of multi-view face
images by which we take the additive white Gaussian noise into considerations.
The results indicated the reliability, robustness of the proposed system with
different poses and variations including noise images.Comment: 7 pages, 4 figures, 4 table
Local Learning with Deep and Handcrafted Features for Facial Expression Recognition
We present an approach that combines automatic features learned by
convolutional neural networks (CNN) and handcrafted features computed by the
bag-of-visual-words (BOVW) model in order to achieve state-of-the-art results
in facial expression recognition. To obtain automatic features, we experiment
with multiple CNN architectures, pre-trained models and training procedures,
e.g. Dense-Sparse-Dense. After fusing the two types of features, we employ a
local learning framework to predict the class label for each test image. The
local learning framework is based on three steps. First, a k-nearest neighbors
model is applied in order to select the nearest training samples for an input
test image. Second, a one-versus-all Support Vector Machines (SVM) classifier
is trained on the selected training samples. Finally, the SVM classifier is
used to predict the class label only for the test image it was trained for.
Although we have used local learning in combination with handcrafted features
in our previous work, to the best of our knowledge, local learning has never
been employed in combination with deep features. The experiments on the 2013
Facial Expression Recognition (FER) Challenge data set, the FER+ data set and
the AffectNet data set demonstrate that our approach achieves state-of-the-art
results. With a top accuracy of 75.42% on FER 2013, 87.76% on the FER+, 59.58%
on AffectNet 8-way classification and 63.31% on AffectNet 7-way classification,
we surpass the state-of-the-art methods by more than 1% on all data sets.Comment: Accepted in IEEE Acces
Going Deeper in Facial Expression Recognition using Deep Neural Networks
Automated Facial Expression Recognition (FER) has remained a challenging and
interesting problem. Despite efforts made in developing various methods for
FER, existing approaches traditionally lack generalizability when applied to
unseen images or those that are captured in wild setting. Most of the existing
approaches are based on engineered features (e.g. HOG, LBPH, and Gabor) where
the classifier's hyperparameters are tuned to give best recognition accuracies
across a single database, or a small collection of similar databases.
Nevertheless, the results are not significant when they are applied to novel
data. This paper proposes a deep neural network architecture to address the FER
problem across multiple well-known standard face datasets. Specifically, our
network consists of two convolutional layers each followed by max pooling and
then four Inception layers. The network is a single component architecture that
takes registered facial images as the input and classifies them into either of
the six basic or the neutral expressions. We conducted comprehensive
experiments on seven publically available facial expression databases, viz.
MultiPIE, MMI, CK+, DISFA, FERA, SFEW, and FER2013. The results of proposed
architecture are comparable to or better than the state-of-the-art methods and
better than traditional convolutional neural networks and in both accuracy and
training time.Comment: To be appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 2016 {Accepted in first round submission
How far did we get in face spoofing detection?
The growing use of control access systems based on face recognition shed
light over the need for even more accurate systems to detect face spoofing
attacks. In this paper, an extensive analysis on face spoofing detection works
published in the last decade is presented. The analyzed works are categorized
by their fundamental parts, i.e., descriptors and classifiers. This structured
survey also brings the temporal evolution of the face spoofing detection field,
as well as a comparative analysis of the works considering the most important
public data sets in the field. The methodology followed in this work is
particularly relevant to observe trends in the existing approaches, to discuss
still opened issues, and to propose new perspectives for the future of face
spoofing detection
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications
Facial expressions are an important way through which humans interact
socially. Building a system capable of automatically recognizing facial
expressions from images and video has been an intense field of study in recent
years. Interpreting such expressions remains challenging and much research is
needed about the way they relate to human affect. This paper presents a general
overview of automatic RGB, 3D, thermal and multimodal facial expression
analysis. We define a new taxonomy for the field, encompassing all steps from
face detection to facial expression recognition, and describe and classify the
state of the art methods accordingly. We also present the important datasets
and the bench-marking of most influential methods. We conclude with a general
discussion about trends, important questions and future lines of research
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Entropy Projection Curved Gabor with Random Forest and SVM for Face Recognition
In this work, we propose a workflow for face recognition under occlusion using the entropy projection from the curved Gabor filter, and create a representative and compact features vector that describes a face. Despite the reduced vector obtained by the entropy projection, it still presents opportunity for further dimensionality reduction. Therefore, we use a Random Forest classifier as an attribute selector, providing a 97% reduction of the original vector while keeping suitable accuracy. A set of experiments using three public image databases: AR Face, Extended Yale B with occlusion and FERET illustrates the proposed methodology, evaluated using the SVM classifier. The results obtained in the experiments show promising results when compared to the available approaches in the literature, obtaining 98.05% accuracy for the complete AR Face, 97.26% for FERET and 81.66% with Yale with 50% occlusion
Evaluation of the Spatio-Temporal features and GAN for Micro-expression Recognition System
Owing to the development and advancement of artificial intelligence, numerous
works were established in the human facial expression recognition system.
Meanwhile, the detection and classification of micro-expressions are attracting
attentions from various research communities in the recent few years. In this
paper, we first review the processes of a conventional optical-flow-based
recognition system, which comprised of facial landmarks annotations, optical
flow guided images computation, features extraction and emotion class
categorization. Secondly, a few approaches have been proposed to improve the
feature extraction part, such as exploiting GAN to generate more image samples.
Particularly, several variations of optical flow are computed in order to
generate optimal images to lead to high recognition accuracy. Next, GAN, a
combination of Generator and Discriminator, is utilized to generate new "fake"
images to increase the sample size. Thirdly, a modified state-of-the-art
Convolutional neural networks is proposed. To verify the effectiveness of the
the proposed method, the results are evaluated on spontaneous micro-expression
databases, namely SMIC, CASME II and SAMM. Both the F1-score and accuracy
performance metrics are reported in this paper.Comment: 15 pages, 16 figures, 6 table
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