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Locality preserving projection (LPP) is a promising manifold learning approach for dimensionality reduction. However, it often encounters small sample size (3S) problem in face recognition tasks. To overcome this limitation, this paper proposes a discrete sine transform (DST) feature extraction approach and develops a DST-feature based LPP algorithm for face recognition. The proposed method has been...
Over the past few years, multi-view face detection issue has become one of the most attractive research topics in the field of computer vision. In this paper, a novel automatic system for multi-view face detection and pose estimation is proposed. Our approach adopts modified appearance-based learning methods to build corresponding face detectors and pose estimators, and detects multi-view faces according...
Beauty is an abstract concept that is inherently difficult to quantify and evaluate. The analysis of facial attractiveness has received much research attention in the past. Recent work has shown that facial attractiveness can be learned by machine, using supervised learning techniques. This paper proposes a computational method for estimating facial attractiveness based on Gabor features and support...
In this paper we have proposed a new way to achieve the optimum learning rate that can reduce the learning time of the multi layer feed forward neural network. The effect of optimum numbers of inner iterations and numbers of hidden nodes on learning time and recognition rate has been shown. The Principal Component Analysis and Multilayer Feed Forward Neural Network are applied in face recognition...
In order to exploit the informative components hidden in nonnegative matrix factorization, an information theoretic learning method, termed ITNMF, is presented. Different from the existing NMF methods, the proposed method is able to handle the general objective optimization, and takes the conjugate gradient technique to enhance the iterative optimization. To tackle the null matrix factorization problem,...
Extracting accurate positions of eyes, nose and mouth, is a crucial process for face recognition and facial expression recognition. Classical methods such as Active Appearance Model (AAM) use the principal component analysis to reduce the dimensionality of appearance data, and an iterative search to find facial features by minimizing an error criteria of the reduced appearance data. In this paper,...
A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this paper. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this...
This paper presents a novel manifold learning method, namely Directional two-dimensional neighborhood preserving embedding (Dir-2DNPE), for feature extraction. In contrast to standard NPE, Dir-2DNPE directly seeks the optimal projective vectors from the directional images without image-to-vector transformation. Moreover, Dir-2DNPE can well reserve the spatial correlations between variations of rows...
Archetypal analysis (AA) proposed by Cutler and Breiman in estimates the principal convex hull of a data set. As such AA favors features that constitute representative 'corners' of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys the interpretability of clustering - without being limited to hard assignment and the uniqueness of SVD - without being limited to orthogonal representations...
Recently, local discriminant embedding (LDE) was proposed to manifold learning and pattern classification. In LDE framework, the neighbor and class of data points were used to construct the graph embedding for classification problems. From a high dimensional to a low dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring data points of...
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...
AdaBoost based training method has become a state-of-the-art boosting approach in face detection system. In this paper, compared to the naive AdaBoost method, Forward Feature Selection (FFS) method is used in feature selection to reduce the training time by about 50 to 100 times without loss of performance. Furthermore, hierarchical feature spaces (both local and global) to construct a detector cascade...
Face recognition has become one of the most important research areas of pattern recognition and machine learning due to its potential applications in many fields. To effectively cope with this problem, a novel face recognition algorithm is proposed by using manifold learning and minimax probability machine. Comprehensive comparisons and extensive experiments show that the proposed algorithm achieves...
Neighborhood Preserving Embedding (NPE) is a subspace learning algorithm, which has the ability of preserving local neighborhood structure on the data manifold. Though NPE has been applied in many domains of pattern recognition, it is a vector-based method and will be encountered the small size sample (SSS) problem when it is directly applied to face recognition. To address this problem, the popular...
The face recognition task involves extraction of unique features from the human face. Manifold learning methods are proposed to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. LPP should be seen as an alternative to Principal Component Analysis (PCA). When the high dimensional data lies on a low dimensional manifold embedded in the ambient...
In this paper, we propose a face detection framework that combines both feature, and skin pixel approaches, while making the framework self adaptive which is important for non controlled environmental conditions. The framework uses skin color information to reduce the search space for faces by localizing the probable skin regions using a mixture of multivariate Gaussians whose parameters are first...
This paper proposes a frontal staircase detection algorithm using both classical Haar-like features and a novel set of PCA-base Haar-like features. Real AdaBoost is used for training a cascaded classifier. The PCA-based Haar-like features are extremely efficient at rejecting background regions at early stages in the cascade. A specifically designed scanning scheme made the algorithm constantly time...
In this paper, we propose a novel supervised learning method called Global Sparse Representation Projections (GSRP) for linear dimensionality reduction. GSRP can be viewed as a combiner of sparse representation and manifold learning. But differing from the recent manifold learning methods such as Local Preserving Projections (LPP), GSRP introduces the global sparse representation information into...
This paper presents a real-time face detection algorithm. It improves state-of-the-art 2D object detection techniques by additionally evaluating a disparity map, which is estimated for the face region using a calibrated stereo camera setup. First, faces are detected in the 2D images with a rapid object classifier based on haar-like features. In a second step, falsely detected faces are removed by...
In this paper, we propose to kernelize linear learning machines, e.g., PCA and LDA, in the empirical kernel feature space, a finite-dimensional embedding space, in which the distances of the data in the kernel feature space are preserved. The empirical kernel feature space provides a unified framework for the kernelization of all kinds of linear machines: performing a linear machine in the finite-dimensional...
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