17,135 research outputs found
Modeling of Facial Aging and Kinship: A Survey
Computational facial models that capture properties of facial cues related to
aging and kinship increasingly attract the attention of the research community,
enabling the development of reliable methods for age progression, age
estimation, age-invariant facial characterization, and kinship verification
from visual data. In this paper, we review recent advances in modeling of
facial aging and kinship. In particular, we provide an up-to date, complete
list of available annotated datasets and an in-depth analysis of geometric,
hand-crafted, and learned facial representations that are used for facial aging
and kinship characterization. Moreover, evaluation protocols and metrics are
reviewed and notable experimental results for each surveyed task are analyzed.
This survey allows us to identify challenges and discuss future research
directions for the development of robust facial models in real-world
conditions
Facial age estimation using BSIF and LBP
Human face aging is irreversible process causing changes in human face
characteristics such us hair whitening, muscles drop and wrinkles. Due to the
importance of human face aging in biometrics systems, age estimation became an
attractive area for researchers. This paper presents a novel method to estimate
the age from face images, using binarized statistical image features (BSIF) and
local binary patterns (LBP)histograms as features performed by support vector
regression (SVR) and kernel ridge regression (KRR). We applied our method on
FG-NET and PAL datasets. Our proposed method has shown superiority to that of
the state-of-the-art methods when using the whole PAL database.Comment: 5 pages, 8 figure
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
Improved graph-based SFA: Information preservation complements the slowness principle
Slow feature analysis (SFA) is an unsupervised-learning algorithm that
extracts slowly varying features from a multi-dimensional time series. A
supervised extension to SFA for classification and regression is graph-based
SFA (GSFA). GSFA is based on the preservation of similarities, which are
specified by a graph structure derived from the labels. It has been shown that
hierarchical GSFA (HGSFA) allows learning from images and other
high-dimensional data. The feature space spanned by HGSFA is complex due to the
composition of the nonlinearities of the nodes in the network. However, we show
that the network discards useful information prematurely before it reaches
higher nodes, resulting in suboptimal global slowness and an under-exploited
feature space.
To counteract these problems, we propose an extension called hierarchical
information-preserving GSFA (HiGSFA), where information preservation
complements the slowness-maximization goal. We build a 10-layer HiGSFA network
to estimate human age from facial photographs of the MORPH-II database,
achieving a mean absolute error of 3.50 years, improving the state-of-the-art
performance. HiGSFA and HGSFA support multiple-labels and offer a rich feature
space, feed-forward training, and linear complexity in the number of samples
and dimensions. Furthermore, HiGSFA outperforms HGSFA in terms of feature
slowness, estimation accuracy and input reconstruction, giving rise to a
promising hierarchical supervised-learning approach.Comment: 40 pages, 9 figures, 9 tables, submitted to Pattern Recognitio
Face Alignment Robust to Pose, Expressions and Occlusions
We propose an Ensemble of Robust Constrained Local Models for alignment of
faces in the presence of significant occlusions and of any unknown pose and
expression. To account for partial occlusions we introduce, Robust Constrained
Local Models, that comprises of a deformable shape and local landmark
appearance model and reasons over binary occlusion labels. Our occlusion
reasoning proceeds by a hypothesize-and-test search over occlusion labels.
Hypotheses are generated by Constrained Local Model based shape fitting over
randomly sampled subsets of landmark detector responses and are evaluated by
the quality of face alignment. To span the entire range of facial pose and
expression variations we adopt an ensemble of independent Robust Constrained
Local Models to search over a discretized representation of pose and
expression. We perform extensive evaluation on a large number of face images,
both occluded and unoccluded. We find that our face alignment system trained
entirely on facial images captured "in-the-lab" exhibits a high degree of
generalization to facial images captured "in-the-wild". Our results are
accurate and stable over a wide spectrum of occlusions, pose and expression
variations resulting in excellent performance on many real-world face datasets
BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation
Age estimation is an important yet very challenging problem in computer
vision. Existing methods for age estimation usually apply a divide-and-conquer
strategy to deal with heterogeneous data caused by the non-stationary aging
process. However, the facial aging process is also a continuous process, and
the continuity relationship between different components has not been
effectively exploited. In this paper, we propose BridgeNet for age estimation,
which aims to mine the continuous relation between age labels effectively. The
proposed BridgeNet consists of local regressors and gating networks. Local
regressors partition the data space into multiple overlapping subspaces to
tackle heterogeneous data and gating networks learn continuity aware weights
for the results of local regressors by employing the proposed bridge-tree
structure, which introduces bridge connections into tree models to enforce the
similarity between neighbor nodes. Moreover, these two components of BridgeNet
can be jointly learned in an end-to-end way. We show experimental results on
the MORPH II, FG-NET and Chalearn LAP 2015 datasets and find that BridgeNet
outperforms the state-of-the-art methods.Comment: CVPR 201
A Hierarchical Probabilistic Model for Facial Feature Detection
Facial feature detection from facial images has attracted great attention in
the field of computer vision. It is a nontrivial task since the appearance and
shape of the face tend to change under different conditions. In this paper, we
propose a hierarchical probabilistic model that could infer the true locations
of facial features given the image measurements even if the face is with
significant facial expression and pose. The hierarchical model implicitly
captures the lower level shape variations of facial components using the
mixture model. Furthermore, in the higher level, it also learns the joint
relationship among facial components, the facial expression, and the pose
information through automatic structure learning and parameter estimation of
the probabilistic model. Experimental results on benchmark databases
demonstrate the effectiveness of the proposed hierarchical probabilistic model.Comment: IEEE Conference on Computer Vision and Pattern Recognition, 201
Dual-reference Face Retrieval
Face retrieval has received much attention over the past few decades, and
many efforts have been made in retrieving face images against pose,
illumination, and expression variations. However, the conventional works fail
to meet the requirements of a potential and novel task --- retrieving a
person's face image at a specific age, especially when the specific 'age' is
not given as a numeral, i.e. 'retrieving someone's image at the similar age
period shown by another person's image'. To tackle this problem, we propose a
dual reference face retrieval framework in this paper, where the system takes
two inputs: an identity reference image which indicates the target identity and
an age reference image which reflects the target age. In our framework, the raw
images are first projected on a joint manifold, which preserves both the age
and identity locality. Then two similarity metrics of age and identity are
exploited and optimized by utilizing our proposed quartet-based model. The
experiments show promising results, outperforming hierarchical methods.Comment: Accepted at AAAI 201
Facial Landmark Detection with Tweaked Convolutional Neural Networks
We present a novel convolutional neural network (CNN) design for facial
landmark coordinate regression. We examine the intermediate features of a
standard CNN trained for landmark detection and show that features extracted
from later, more specialized layers capture rough landmark locations. This
provides a natural means of applying differential treatment midway through the
network, tweaking processing based on facial alignment. The resulting Tweaked
CNN model (TCNN) harnesses the robustness of CNNs for landmark detection, in an
appearance-sensitive manner without training multi-part or multi-scale models.
Our results on standard face landmark detection and face verification
benchmarks show TCNN to surpasses previously published performances by wide
margins.Comment: First two authors had joint first authorship / equal contributio
Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model
Automatic age estimation from real-world and unconstrained face images is
rapidly gaining importance. In our proposed work, a deep CNN model that was
trained on a database for face recognition task is used to estimate the age
information on the Adience database. This paper has three significant
contributions in this field. (1) This work proves that a CNN model, which was
trained for face recognition task, can be utilized for age estimation to
improve performance; (2) Over fitting problem can be overcome by employing a
pretrained CNN on a large database for face recognition task; (3) Not only the
number of training images and the number subjects in a training database effect
the performance of the age estimation model, but also the pre-training task of
the employed CNN determines the performance of the model.Comment: 8 pages, 2 figure
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