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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...
This paper proposes a two-step prototype-face-based scheme of hallucinating the high-resolution detail of a low-resolution input face image. The proposed scheme is mainly composed of two steps: the global estimation step and the local facial-parts refinement step. In the global estimation step, the initial high-resolution face image is hallucinated via a linear combination of the global prototype...
This paper describes and deduces the theory of Haar-like features, Integral image and AdaBoost algorithm, which were proposed by Paul Viola, and then researches its improvement. We combine Microsoft Visual C++6.0 with OpenCV Function library to develop the software, and achieve the function of real-time face detection. According to experimental results, we can conclude that the improved algorithm...
Expression variation is one of the most important factors that considerably influence the performance of face recognition. In order to enhance robustness to expression variations, a procedure of 3D face recognition based on depth image and SURF Operator is proposed. We use Fisher Linear Discriminant (FLD) method on the depth image to perform coarse recognition first to catch the highly ranked 3D faces...
In recent years, research on dictionary design for sparse representation (SR) has changed from pre-defined to training. A Hierarchical K-means Clustering (HKC) dictionary training algorithm is proposed in this paper. The algorithm presents a framework for SR for a class of images. HKC used K-means clustering to generate atoms which is one to one corresponding to hyperplanes for approximating hyperspherical...
Image inpainting is one of the challenging problems in image restoration. To recover the missing region, we can only rely on the information in the uncorrupted region of the input image and some prior knowledge. The latter can be learned from suitable training data or implemented through some smoothness constraints. In this paper, a new approach for image inpainting is proposed. Here, we iteratively...
The data in face images are distributed in a complex manner due to the variation of light intensity, facial expression and pose. In this paper the Kernel Principal Component Analysis (KPCA) is used to extract the feature set of male and female faces. A Gaussian model of skin segmentation method is applied here to exclude the global features such as beard, eyebrow, moustache, etc. both training and...
Face recognition is one of the most active research areas in computer vision and pattern recognition with practical applications. This work proposes an appearance based eigenface technique. PCA is used in extracting the relevant information in human faces. In this method the eigenvectors of the set of training images are calculated which define the face space. Face images are projected on to the face...
Principal component analysis (PCA) and Fisher discriminate analysis (FDA) of holistic approach of Information theory have been analyzed. Two steps for recognition are taken: training and testing. In the training phase a set of the eigenvectors of the covariance matrix of the images used for training. These eigenvectors are also called as eigenfaces. In testing phase when a new input image is given...
Here an efficient fusion technique for automatic face recognition has been presented. Fusion of visual and thermal images has been done to take the advantages of thermal images as well as visual images. By employing fusion a new image can be obtained, which provides the most detailed, reliable, and discriminating information. In this method fused images are generated using visual and thermal face...
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...
The important issue in multi-class classification on support vector machines is the decision rule, which determines whether an input pattern belongs to a predicted class. To enhance the accuracy of multi-class classification, this study proposes a multi-weighted majority voting algorithm of support vector machine (SVM), and applies it to overcome complex facial security application. The proposed algorithm...
This paper presents a new approach for face detection based on eigenfaces/principal component analysis (PCA) and Legendre moments (LM). PCA and Legendre moments are two different methods used for detecting patterns in images. We present a hybrid system for face detection which combines the eigen weights calculated by PCA and Legendre moments calculated by Legendre polynomial together. These combined...
This paper presents a projection model to fuse the scores of a visual face verification system and an infrared face verification system. Essentially, the model consists of an arbitrarily number of linear projection vectors with randomly permuted elements. An equal error rate formulation is next adopted to learn the linear coefficients for projection. The learned model is consequently used for prediction...
Automatic face recognition is a challenging problem, since human faces have a complex pattern. This paper presents a technique for recognition of frontal human faces on gray scale images. In this technique, the distance between the Discrete Cosine Transform (DCT) of the face under evaluation and all the DCTs of the faces database are computed. The faces with the shortest distances probably belong...
We have designed and implemented a multi-linear discriminant method of constructing and quantifying statistically significant changes on human identity photographs. The method is based on a general multivariate two-stage linear framework that addresses the small sample size problem in high-dimensional spaces. Starting with a 2D face data set of well framed images, we determine a most characteristic...
Locating and identifying complex objects in a visual scene is a typical problem within the areas of computer vision and image analysis. One technique to minimise the size of image to be identified is to base the classification on smaller features of the image, which are combined into a more complex structure to identify the complete object. For example, locating two eyes, a nose and a mouth can enable...
In this paper, we propose an efficient method to compute the optimal discriminant vectors of Generalized Discriminant Analysis (GDA) for face recognition tasks. The optimal discriminative features of face images are obtained by directly performing the kernel Gram-Schmidt orthogonalization procedure on the difference vectors only once. The theoretical justification is presented. The nonlinear difference...
Support vector machine is a new machine learning technique developed on the basis of statistical learning theory, which has become the hotspot of machine learning because of its excellent learning performance. Based on analyzing the theory of support vector machine for regression (SVR), a SVR model is established for predicting the output in fully mechanized mining face, and then realizes the model...
Regularization plays a vital role in ill-posed problems. A properly chosen regularization can direct the solution toward a better quality outcome. An emerging powerful regularization is one that leans on image examples. In this paper, we propose a novel scheme for face hallucination. We target specially the quality of highly zoomed outputs. Our work bases on the pyramid framework and assigns several...
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