The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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...
By maximizing the gap between classes in the reproducing kernel Hilbert space (RKHS), our method optimizes for the sigma values of radial basis function (RBF) or gaussian kernels. For each sample, we try to ensure the distance gap between intra-class and inter-class in RKHS to be large. Unlike previous methods of multiple kernel learning, our method does not need large amount of computations, which...
Local appearance descriptors are widely used on facial emotion recognition tasks. With these descriptors, image filters, such as Gabor wavelet or local binary patterns (LBP) are applied on the whole or specific regions of the face to extract facial appearance changes. But it is also clear that beside feature descriptor; choice of suitable learning method that integrates feature novelty is vital. The...
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...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.