38,536 research outputs found
FACE RECOGNITION AND VERIFICATION IN UNCONSTRAINED ENVIRIONMENTS
Face recognition has been a long standing problem in computer vision. General
face recognition is challenging because of large appearance variability due to
factors including pose, ambient lighting, expression, size of the face, age, and distance
from the camera, etc. There are very accurate techniques to perform face
recognition in controlled environments, especially when large numbers of samples
are available for each face (individual). However, face identification under uncontrolled(
unconstrained) environments or with limited training data is still an unsolved
problem. There are two face recognition tasks: face identification (who is who in
a probe face set, given a gallery face set) and face verification (same or not, given
two faces). In this work, we study both face identification and verification in unconstrained
environments.
Firstly, we propose a face verification framework that combines Partial Least
Squares (PLS) and the One-Shot similarity model[1]. The idea is to describe a
face with a large feature set combining shape, texture and color information. PLS
regression is applied to perform multi-channel feature weighting on this large feature
set. Finally the PLS regression is used to compute the similarity score of an image
pair by One-Shot learning (using a fixed negative set).
Secondly, we study face identification with image sets, where the gallery and
probe are sets of face images of an individual. We model a face set by its covariance
matrix (COV) which is a natural 2nd-order statistic of a sample set.By exploring an
efficient metric for the SPD matrices, i.e., Log-Euclidean Distance (LED), we derive
a kernel function that explicitly maps the covariance matrix from the Riemannian
manifold to Euclidean space. Then, discriminative learning is performed on the
COV manifold: the learning aims to maximize the between-class COV distance and
minimize the within-class COV distance.
Sparse representation and dictionary learning have been widely used in face
recognition, especially when large numbers of samples are available for each face
(individual). Sparse coding is promising since it provides a more stable and discriminative
face representation. In the last part of our work, we explore sparse
coding and dictionary learning for face verification application. More specifically,
in one approach, we apply sparse representations to face verification in two ways
via a fix reference set as dictionary. In the other approach, we propose a dictionary
learning framework with explicit pairwise constraints, which unifies the discriminative
dictionary learning for pair matching (face verification) and classification (face
recognition) problems
Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs
State-of-the-art image-set matching techniques typically implicitly model
each image-set with a Gaussian distribution. Here, we propose to go beyond
these representations and model image-sets as probability distribution
functions (PDFs) using kernel density estimators. To compare and match
image-sets, we exploit Csiszar f-divergences, which bear strong connections to
the geodesic distance defined on the space of PDFs, i.e., the statistical
manifold. Furthermore, we introduce valid positive definite kernels on the
statistical manifolds, which let us make use of more powerful classification
schemes to match image-sets. Finally, we introduce a supervised dimensionality
reduction technique that learns a latent space where f-divergences reflect the
class labels of the data. Our experiments on diverse problems, such as
video-based face recognition and dynamic texture classification, evidence the
benefits of our approach over the state-of-the-art image-set matching methods
Large Margin Image Set Representation and Classification
In this paper, we propose a novel image set representation and classification
method by maximizing the margin of image sets. The margin of an image set is
defined as the difference of the distance to its nearest image set from
different classes and the distance to its nearest image set of the same class.
By modeling the image sets by using both their image samples and their affine
hull models, and maximizing the margins of the images sets, the image set
representation parameter learning problem is formulated as an minimization
problem, which is further optimized by an expectation -maximization (EM)
strategy with accelerated proximal gradient (APG) optimization in an iterative
algorithm. To classify a given test image set, we assign it to the class which
could provide the largest margin. Experiments on two applications of
video-sequence-based face recognition demonstrate that the proposed method
significantly outperforms state-of-the-art image set classification methods in
terms of both effectiveness and efficiency
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