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In this paper, an improved Kernel Fisher Discriminant (KFD) method is used in face recognition. A Generalized Kernel Fisher Discriminant Analysis (GKFD) is proposed to make the most of two kinds of discriminant information in “double discriminant subspaces”. It can also uniform the discriminant functions in two subspaces of DSDA. Experimental results on ORL face database show the feasibility of the...
Airborne cameras on low-flying unmanned vehicles introduce new privacy challenges due to their mobility and viewing angles. In this paper, we focus on face recognition from airborne cameras and explore the design space to determine when a face in an airborne image is inherently protected, that is when an individual is not recognizable. Moreover, when individuals are recognizable by facial recognition...
Face alignment is very crucial to the task of face attributes recognition. The performance of face attributes recognition would notably degrade if the fiducial points of the original face images are not precisely detected due to large lighting, pose and occlusion variations. In order to alleviate this problem, we propose a spatial transform based deep CNNs to improve the performance of face attributes...
Traditional nonnegative matrix factorization (NMF) is an unsupervised method for linear feature extraction. Recently, NMF with block strategy is shown to be able to extract more sparse and discriminative information of the images. To enhance the discriminative power of NMF, this paper proposes a block kernel nonnegative matrix factorization (BKNMF) based on the kernel theory and block technique. Kernel...
In this paper, we propose a new dimensionality reduction method called discriminative sparsity preserving graph embedding (DSPGE). Unlike many existing graph embedding methods such as locality preserving projections (LPP) and sparsity preserving projections (SPP), the aim of DSPGE is to preserve the sparse reconstructive relationships of data while simultaneously capture the geometric and discriminant...
This paper presents an illumination invariant face recognition system that uses local directional pattern descriptor and modular histogram. The proposed Modular Histogram of Oriented Directional Features (MHODF) is an oriented local descriptor that is able to encode various patterns of face images under different lighting conditions. It employs the edge response values in different directions to encode...
Deep neural networks (DNNs) have been successfully applied in the fields of computer vision and pattern recognition. One drawback of DNNs is that most of existing DNNs models and their variants usually need to learn a very large set of parameters. Another drawback of DNNs is that DNNs does not fully take the class label and local structure into account during the training stage. To address these issues,...
In this paper, we present the Kernel Subclass Support Vector Data Description classifier. We focus on face recognition and human action recognition applications, where we argue that sub-classes are formed within the training class. We modify the standard SVDD optimization problem, so that it exploits subclass information in its optimization process. We extend the proposed method to work in feature...
In this paper, an asymmetric kernel is proposed for extracting sparse features from two-dimensional visual face images for identity recognition. Essentially, the kernel consists of an inner product of two vectors where one of them has been raised to power terms element-wise. The impact of such a power term is suppression of less influential features where only relevant ones are used for estimation...
Sparsity based signal processing is a relatively new research area which has attracted tremendous interest from researchers. Application areas for sparse signal processing include but are not limited to image processing, pattern recognition and computer vision. This work considers the joint application of sparsity and kernel methods to classification problems. Novel sparsity based classifiers have...
We present a multi-modal feature fusion framework for Kinect-based Facial Expression Recognition (FER). The framework extracts and pre-processes 2D and 3D features separately. The types of 2D and 3D features are selected to maximize the accuracy of the system, with the Histogram of Oriented Gradient (HOG) features for 2D data and statistically selected angles for 3D data giving the best performance...
Automatic facial image analysis has received considerable research interests due to its important role in computer vision and biometrics. As the key component, face feature is usually extracted under largely controlled environment and learnt for specific tasks which limits its discriminant capability in a multi-task learning scenario. In this paper, we present a novel deeply learnt tree-structured...
Traditional face recognition methods such as Principal Components Analysis(PCA), Independent Component Analysis(ICA) and Linear Discriminant Analysis(LDA) are linear discriminant methods, but in the real situation, a lot of problems can't be linear discriminated; therefore, researchers proposed face recognition method based on kernel techniques which can transform the nonlinear problem of inputting...
Face recognition (FR) has received significant attention as one of the most successful applications of image analysis and understanding, during the past several years and is an active yet challenging topic in computer vision applications. Also potentially will help in identifying ultra-rare and developmental disorders. Linear discriminant analysis (LDA) has been widely used for feature extraction...
Sparse-representation is well-known for its promising performance in face recognition task. Recently, researchers have focused on optimizing the dictionary by learning the discriminative sparse model. On the other hand, symmetric positive definite (SPD) matrix descriptor has spurred great interest among computer vision community due to its inherent merits that enables features fusion. However SPD...
Traditional Nonnegative Matrix Factorization (NMF) is a linear and unsupervised algorithm. This would limit the classification power of NMF for the complicated data. To overcome the above limitations of NMF, this paper proposes a novel supervised and nonlinear NMF algorithm based on kernel theory and discriminant analysis. We incorporate the class label information into the decomposition of NMF in...
Local Binary patterns (LBP) and its extensions typify the present face descriptors due to their intrinsic capability of featuring the neighborhood changes striding over every pixel. These descriptors are usually engineered in an obsolete handcrafted manner and thus sufficient prior knowledge and expertise are necessitated to assure the recognition performance. This paper outlines an improved face...
We have applied Latent Topic Models to facial expression recognition. We showed that the latent topic learned from a topic model is very similar to the Action Units defined by psychologists in the Facial Action Coding Systems (FACS). Furthermore, we noted that the topics thus obtained may be correlated with each other, and we tried to model this by the correlated topic model (CTM). Preliminary results...
Kernel Principal Component Analysis (KPCA) is a popular feature extraction technique for face recognition. However, it often suffers from the high computational complexity problem, when dealing with large samples. Besides, KPCA is a holistic feature based approach, which means that it discards some useful discriminate local information. In this paper, we use Random Nonlinear Principal Component Analysis...
Enhanced Local Ternary Patterns (ELTP) significantly improves performance over other feature descriptor methods including Local Binary Patterns (LBP) and Local Ternary Patterns (LTP).Sequential implementation of ELTP results in poor performance in terms of execution time for real time systems.Speed and accuracy are important characteristics of a real time face recognition system. With the aim of fulfilling...
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