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In Face recognition, a combination of neural network (NN), known as an ensemble of neural network, often outperforms individual ones. This paper is aiming to present a support vector machines (SVM)-ensemble-based efficient face recognition system. The training samples are randomly chosen by means of bootstrap technique to train the different SVM independently. These SVM's are combined together to...
The pattern recognition in the sparse representation (SR) framework has been very successful. In this model, the test sample can be represented as a sparse linear combination of training samples by solving a norm-regularized least squares problem. However, the value of regularization parameter is always indiscriminating for the whole dictionary. To enhance the group concentration of the coefficients...
Multi-view representations are widely existed in practical applications, the quality of latent representation learned from multi-view observations often suffer from noise and outliers in original data. In this work, we propose an auto encoder based deep multi-view robust representation learning (DMRRL) algorithm, which can learn a shared representation from multi-view observations and the algorithm...
In this paper, we suggested AdBoost algorithm for further improvising the performance of system. In the enhanced adaboost, the eigen vectors are computed for facial region & applied classification. In the process of classification, we opt for process of learning, training & testing. As observed from the result sessions in the previous paper [13] the outcomes from the reboost detection are...
Security system based on biometrics is becoming more popular everyday as a part of safety and security measurement against all kind of crimes. Among several kinds of biometric security systems, face recognition is one of the most popular one. It is one of the most accurate, mostly used recognition methods in modern world. In this paper, two most popular face recognition methods have been discussed...
The two-dimensional direct Linear Discriminant Analysis (2D-DLDA) algorithm is based on the direct LDA and two-dimensional LDA. The algorithm retains the useful null space and uses the original two-dimensional image matrix directly while it does not pay much attention to the influence of the edge class and the overlap of the classes. So an improved method of 2D-DLDA is proposed in this paper. This...
This research presents framework for real time face recognition and face emotion detection system based on facial features and their actions. The key elements of Face are considered for prediction of face emotions and the user. The variations in each facial feature are used to determine the different emotions of face. Machine learning algorithms are used for recognition and classification of different...
Linear Discriminant Regression Classification (LDRC) is an effective method developed in the recent years on aim of providing enhancement to the accuracy of Face Recognition (FR) based systems. The visible general problems in face recognition are fraudulent faces and the factors affecting recognition accuracy such as noise, diversions in the angle, poses and expression. These problems are the main...
Face recognition has been receiving continuous academic and commercial attention for the last decades. In this paper, we construct two face recognition systems adopting SVM and Adaboost as the classifiers with fast PCA for facial feature representation. The detailed discussions about algorithm realization are given. Comparison between the two systems and analysis of them are provided through several...
In this paper, a facial expression recognition algorithm based on Gabor and conditional random fields is proposed. Firstly, owing to the fact that in the existing databases, the number of people and images are relatively small, we established our own facial expression database, and some preprocessing methods are performed thereon. Secondly, Gabor features are extracted in five scales and eight directions...
Human face recognition technology usually takes advantages of two-dimensional or three-dimensional data. Rising from 1980s, three-dimensional face recognition technology soon become one of the headed topic because of its admirable resistance to interference and more information compared with two-dimensional face recognition technology. The new 3D face model standardization algorithm presented in this...
Previous dictionary learning algorithms usually take the locality information of training samples into account in the learning process, and it may degrade the robustness of the dictionary. In this paper, an new locality constrained dictionary learning algorithm (LCDL) for face recognition by using the locality characters of atoms is proposed. Since the atoms are learned from the training samples,...
Sparse Representation based Classifier (SRC) is widely used in a variety of pattern recognition and machine learning tasks. The kernelized version of the classifier (Kernel SRC) attempts to remove the SRC limitations by a nonlinear mapping of the data through kernel functions. However, the performance of such a method strongly relies on the choice of the kernel function. In this paper, we firstly...
Face recognition under uncontrolled environment persists to be an unresolved problem having challenges such as varying pose, illumination, occlusion etc. In this research, we propose an algorithm for identification of faces with pose and illumination variations. An adaptive dictionary learning framework built upon group sparse representation classifier is presented in order to learn dictionary parameters...
In this paper, we propose a new framework for facial expression classification. This framework utilizes random forest as the classifier based on the features extracted from improved principal component analysis (PCA). Traditional PCA has two drawbacks: it is difficult to estimate the covariance matrix, and it is computational prohibitive to get the eigenvectors. In order to solve the two problems,...
In this paper, a new fast learning algorithm named deterministic learning machine (DLM) for the training of single-hidden layer feed-forward neural network (SLFN) subject to face recognition problem is proposed to solve the problem of high dimensional pattern recognition. The existing training algorithms for SLFN are either gradient based iterative learning algorithms or non-iterative algorithms such...
Different pixel plays different roles in representing a face image. In this paper, we proposed a novel representation which integrates original and its virtual face image to represent test sample. This method first combines two adjacent columns of an original face image to generate corresponding virtual face image, and then classification algorithm is respectively applied to the original and virtual...
The typical sparse representation for classification (SRC) can obtain desirable recognition result when the training samples in each class are sufficient. Nevertheless, if the training sample set is small scale, i.e., each class has a few training samples, even single sample, the traditional SRC cannot perform well. Although one of the variants of the traditional SRC, the extended SRC(ESRC), can effectively...
Face recognition based on sparse representation is investigated in this paper. Dimensionality reduction is the process of projecting original image data into a low dimensional space, and usually be conducted before dictionary learning. However dimensionality reduction may lose information that is important to the face recognition task. Although the accuracy rate of face recognition varies with different...
Face recognition is one of the most natural biometrics identification approaches. But the accuracy of face recognition is significantly influenced by poses, facial expressions, and lighting conditions. This paper presents a novel robust facial recognition system by incorporating color information into a modified active shape model (ASM). The selected robust geometric and chromatic information of facial...
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