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Local binary pattern (LBP) has limitation in extracting the edge and direction information, which is vital to infrared face recognition. A new infrared face recognition algorithm fusion of LBP and histogram of oriented gradients (HOG) is proposed. First, LBP operator is adopted to extract the texture feature of an infrared face, and then the edge features of the original infrared face are extracted...
Recent advances in multiple-kernel learning (MKL) show the effectiveness to fuse multiple base features in object detection and recognition. However, MKL tends to select only the most discriminative base features but ignore other less discriminative base features which may provide complementary information. Moreover, MKL usually employ Gaussian RBF kernels to transform each base feature to its high...
Spectral clustering (SC) has become one of the most popular clustering methods. Given an affinity matrix, SC explores its spectral-graph structure to partition data into disjoint meaningful groups. However, in many applications, there are multiple potentially useful features and thereby multiple affinity matrices. For applying spectral clustering to such cases, these affinity matrices must be aggregated...
Kernel nonnegative matrix factorization (KNMF) is a recent kernel extension of NMF, where matrix factorization is carried out in a reproducing kernel Hilbert space (RKHS) with a feature mapping φ(·). Given a data matrix X ∈ ℝm×n, KNMF seeks a decomposition, φ(X) ≈ UV ⊤, where the basis matrix takes the form U = φ (X) W and parameters W ∈ ℝ+n×r and V ∈ ℝ+n×r are estimated without explicit knowledge...
Local image features have been proven to be a powerful way to describe pattern of interest, both from single objects and complex scenes. While learning from images represented by local features is challenging, recent publications and developments in object recognition has shown that significant performance achievements can be achieved by carefully combining multi-level, coarse-to-fine, sparsely distributed...
In recent years, large databases of natural images have become increasingly popular in the evaluation of face and object recognition algorithms. However, Pinto et al. previously illustrated an inherent danger in using such sets, showing that an extremely basic recognition system, built on a trivial feature set, was able to take advantage of low-level regularities in popular object and face recognition...
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