1,460 research outputs found
KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization
We consider the image classification problem via kernel collaborative
representation classification with locality constrained dictionary (KCRC-LCD).
Specifically, we propose a kernel collaborative representation classification
(KCRC) approach in which kernel method is used to improve the discrimination
ability of collaborative representation classification (CRC). We then measure
the similarities between the query and atoms in the global dictionary in order
to construct a locality constrained dictionary (LCD) for KCRC. In addition, we
discuss several similarity measure approaches in LCD and further present a
simple yet effective unified similarity measure whose superiority is validated
in experiments. There are several appealing aspects associated with LCD. First,
LCD can be nicely incorporated under the framework of KCRC. The LCD similarity
measure can be kernelized under KCRC, which theoretically links CRC and LCD
under the kernel method. Second, KCRC-LCD becomes more scalable to both the
training set size and the feature dimension. Example shows that KCRC is able to
perfectly classify data with certain distribution, while conventional CRC fails
completely. Comprehensive experiments on many public datasets also show that
KCRC-LCD is a robust discriminative classifier with both excellent performance
and good scalability, being comparable or outperforming many other
state-of-the-art approaches
Fuzzy Sparse Autoencoder Framework for Single Image Per Person Face Recognition
The issue of single sample per person (SSPP) face recognition has attracted more and more attention in recent years. Patch/local-based algorithm is one of the most popular categories to address the issue, as patch/local features are robust to face image variations. However, the global discriminative information is ignored in patch/local-based algorithm, which is crucial to recognize the nondiscriminative region of face images. To make the best of the advantage of both local information and global information, a novel two-layer local-to-global feature learning framework is proposed to address SSPP face recognition. In the first layer, the objective-oriented local features are learned by a patch-based fuzzy rough set feature selection strategy. The obtained local features are not only robust to the image variations, but also usable to preserve the discrimination ability of original patches. Global structural information is extracted from local features by a sparse autoencoder in the second layer, which reduces the negative effect of nondiscriminative regions. Besides, the proposed framework is a shallow network, which avoids the over-fitting caused by using multilayer network to address SSPP problem. The experimental results have shown that the proposed local-to-global feature learning framework can achieve superior performance than other state-of-the-art feature learning algorithms for SSPP face recognition
Improving Sparse Representation-Based Classification Using Local Principal Component Analysis
Sparse representation-based classification (SRC), proposed by Wright et al.,
seeks the sparsest decomposition of a test sample over the dictionary of
training samples, with classification to the most-contributing class. Because
it assumes test samples can be written as linear combinations of their
same-class training samples, the success of SRC depends on the size and
representativeness of the training set. Our proposed classification algorithm
enlarges the training set by using local principal component analysis to
approximate the basis vectors of the tangent hyperplane of the class manifold
at each training sample. The dictionary in SRC is replaced by a local
dictionary that adapts to the test sample and includes training samples and
their corresponding tangent basis vectors. We use a synthetic data set and
three face databases to demonstrate that this method can achieve higher
classification accuracy than SRC in cases of sparse sampling, nonlinear class
manifolds, and stringent dimension reduction.Comment: Published in "Computational Intelligence for Pattern Recognition,"
editors Shyi-Ming Chen and Witold Pedrycz. The original publication is
available at http://www.springerlink.co
A survey of face recognition techniques under occlusion
The limited capacity to recognize faces under occlusions is a long-standing
problem that presents a unique challenge for face recognition systems and even
for humans. The problem regarding occlusion is less covered by research when
compared to other challenges such as pose variation, different expressions,
etc. Nevertheless, occluded face recognition is imperative to exploit the full
potential of face recognition for real-world applications. In this paper, we
restrict the scope to occluded face recognition. First, we explore what the
occlusion problem is and what inherent difficulties can arise. As a part of
this review, we introduce face detection under occlusion, a preliminary step in
face recognition. Second, we present how existing face recognition methods cope
with the occlusion problem and classify them into three categories, which are
1) occlusion robust feature extraction approaches, 2) occlusion aware face
recognition approaches, and 3) occlusion recovery based face recognition
approaches. Furthermore, we analyze the motivations, innovations, pros and
cons, and the performance of representative approaches for comparison. Finally,
future challenges and method trends of occluded face recognition are thoroughly
discussed
- …