8,042 research outputs found
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
Sparsity-based representations have recently led to notable results in
various visual recognition tasks. In a separate line of research, Riemannian
manifolds have been shown useful for dealing with features and models that do
not lie in Euclidean spaces. With the aim of building a bridge between the two
realms, we address the problem of sparse coding and dictionary learning over
the space of linear subspaces, which form Riemannian structures known as
Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into
the space of symmetric matrices by an isometric mapping. This in turn enables
us to extend two sparse coding schemes to Grassmann manifolds. Furthermore, we
propose closed-form solutions for learning a Grassmann dictionary, atom by
atom. Lastly, to handle non-linearity in data, we extend the proposed Grassmann
sparse coding and dictionary learning algorithms through embedding into Hilbert
spaces.
Experiments on several classification tasks (gender recognition, gesture
classification, scene analysis, face recognition, action recognition and
dynamic texture classification) show that the proposed approaches achieve
considerable improvements in discrimination accuracy, in comparison to
state-of-the-art methods such as kernelized Affine Hull Method and
graph-embedding Grassmann discriminant analysis.Comment: Appearing in International Journal of Computer Visio
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
A survey on mouth modeling and analysis for Sign Language recognition
© 2015 IEEE.Around 70 million Deaf worldwide use Sign Languages (SLs) as their native languages. At the same time, they have limited reading/writing skills in the spoken language. This puts them at a severe disadvantage in many contexts, including education, work, usage of computers and the Internet. Automatic Sign Language Recognition (ASLR) can support the Deaf in many ways, e.g. by enabling the development of systems for Human-Computer Interaction in SL and translation between sign and spoken language. Research in ASLR usually revolves around automatic understanding of manual signs. Recently, ASLR research community has started to appreciate the importance of non-manuals, since they are related to the lexical meaning of a sign, the syntax and the prosody. Nonmanuals include body and head pose, movement of the eyebrows and the eyes, as well as blinks and squints. Arguably, the mouth is one of the most involved parts of the face in non-manuals. Mouth actions related to ASLR can be either mouthings, i.e. visual syllables with the mouth while signing, or non-verbal mouth gestures. Both are very important in ASLR. In this paper, we present the first survey on mouth non-manuals in ASLR. We start by showing why mouth motion is important in SL and the relevant techniques that exist within ASLR. Since limited research has been conducted regarding automatic analysis of mouth motion in the context of ALSR, we proceed by surveying relevant techniques from the areas of automatic mouth expression and visual speech recognition which can be applied to the task. Finally, we conclude by presenting the challenges and potentials of automatic analysis of mouth motion in the context of ASLR
Toward Open-Set Face Recognition
Much research has been conducted on both face identification and face
verification, with greater focus on the latter. Research on face identification
has mostly focused on using closed-set protocols, which assume that all probe
images used in evaluation contain identities of subjects that are enrolled in
the gallery. Real systems, however, where only a fraction of probe sample
identities are enrolled in the gallery, cannot make this closed-set assumption.
Instead, they must assume an open set of probe samples and be able to
reject/ignore those that correspond to unknown identities. In this paper, we
address the widespread misconception that thresholding verification-like scores
is a good way to solve the open-set face identification problem, by formulating
an open-set face identification protocol and evaluating different strategies
for assessing similarity. Our open-set identification protocol is based on the
canonical labeled faces in the wild (LFW) dataset. Additionally to the known
identities, we introduce the concepts of known unknowns (known, but
uninteresting persons) and unknown unknowns (people never seen before) to the
biometric community. We compare three algorithms for assessing similarity in a
deep feature space under an open-set protocol: thresholded verification-like
scores, linear discriminant analysis (LDA) scores, and an extreme value machine
(EVM) probabilities. Our findings suggest that thresholding EVM probabilities,
which are open-set by design, outperforms thresholding verification-like
scores.Comment: Accepted for Publication in CVPR 2017 Biometrics Worksho
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