7,691 research outputs found
On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly
In the field of face recognition, Sparse Representation (SR) has received
considerable attention during the past few years. Most of the relevant
literature focuses on holistic descriptors in closed-set identification
applications. The underlying assumption in SR-based methods is that each class
in the gallery has sufficient samples and the query lies on the subspace
spanned by the gallery of the same class. Unfortunately, such assumption is
easily violated in the more challenging face verification scenario, where an
algorithm is required to determine if two faces (where one or both have not
been seen before) belong to the same person. In this paper, we first discuss
why previous attempts with SR might not be applicable to verification problems.
We then propose an alternative approach to face verification via SR.
Specifically, we propose to use explicit SR encoding on local image patches
rather than the entire face. The obtained sparse signals are pooled via
averaging to form multiple region descriptors, which are then concatenated to
form an overall face descriptor. Due to the deliberate loss spatial relations
within each region (caused by averaging), the resulting descriptor is robust to
misalignment & various image deformations. Within the proposed framework, we
evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder
Neural Network (SANN), and an implicit probabilistic technique based on
Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and
ChokePoint datasets show that the proposed local SR approach obtains
considerably better and more robust performance than several previous
state-of-the-art holistic SR methods, in both verification and closed-set
identification problems. The experiments also show that l1-minimisation based
encoding has a considerably higher computational than the other techniques, but
leads to higher recognition rates
Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification
We propose a robust approach for performing automatic species-level
recognition of fossil pollen grains in microscopy images that exploits both
global shape and local texture characteristics in a patch-based matching
methodology. We introduce a novel criteria for selecting meaningful and
discriminative exemplar patches. We optimize this function during training
using a greedy submodular function optimization framework that gives a
near-optimal solution with bounded approximation error. We use these selected
exemplars as a dictionary basis and propose a spatially-aware sparse coding
method to match testing images for identification while maintaining global
shape correspondence. To accelerate the coding process for fast matching, we
introduce a relaxed form that uses spatially-aware soft-thresholding during
coding. Finally, we carry out an experimental study that demonstrates the
effectiveness and efficiency of our exemplar selection and classification
mechanisms, achieving accuracy on a difficult fine-grained species
classification task distinguishing three types of fossil spruce pollen.Comment: CVMI 201
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution
Wisely utilizing the internal and external learning methods is a new
challenge in super-resolution problem. To address this issue, we analyze the
attributes of two methodologies and find two observations of their recovered
details: 1) they are complementary in both feature space and image plane, 2)
they distribute sparsely in the spatial space. These inspire us to propose a
low-rank solution which effectively integrates two learning methods and then
achieves a superior result. To fit this solution, the internal learning method
and the external learning method are tailored to produce multiple preliminary
results. Our theoretical analysis and experiment prove that the proposed
low-rank solution does not require massive inputs to guarantee the performance,
and thereby simplifying the design of two learning methods for the solution.
Intensive experiments show the proposed solution improves the single learning
method in both qualitative and quantitative assessments. Surprisingly, it shows
more superior capability on noisy images and outperforms state-of-the-art
methods
Recurrent Attention Models for Depth-Based Person Identification
We present an attention-based model that reasons on human body shape and
motion dynamics to identify individuals in the absence of RGB information,
hence in the dark. Our approach leverages unique 4D spatio-temporal signatures
to address the identification problem across days. Formulated as a
reinforcement learning task, our model is based on a combination of
convolutional and recurrent neural networks with the goal of identifying small,
discriminative regions indicative of human identity. We demonstrate that our
model produces state-of-the-art results on several published datasets given
only depth images. We further study the robustness of our model towards
viewpoint, appearance, and volumetric changes. Finally, we share insights
gleaned from interpretable 2D, 3D, and 4D visualizations of our model's
spatio-temporal attention.Comment: Computer Vision and Pattern Recognition (CVPR) 201
FAME: Face Association through Model Evolution
We attack the problem of learning face models for public faces from
weakly-labelled images collected from web through querying a name. The data is
very noisy even after face detection, with several irrelevant faces
corresponding to other people. We propose a novel method, Face Association
through Model Evolution (FAME), that is able to prune the data in an iterative
way, for the face models associated to a name to evolve. The idea is based on
capturing discriminativeness and representativeness of each instance and
eliminating the outliers. The final models are used to classify faces on novel
datasets with possibly different characteristics. On benchmark datasets, our
results are comparable to or better than state-of-the-art studies for the task
of face identification.Comment: Draft version of the stud
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