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PCANet is a simple network using Principal Component Analysis (PCA) for image classification and obtained high accuracies on a variety of datasets. PCA projects explanatory variables on a subspace that the first component has the largest variance. On the other hand, Partial Least Squares (PLS) regression projects explanatory variables on a subspace that the first component has the largest covariance...
In this paper, a scheme to identify the type of script of printed documents for multi-script OCR system is presented. This scheme works on block or paragraph of the given printed document. The proposed scheme uses Wavelet features and MPEG-7 Edge Histogram Descriptor (EHD) feature applied on the wavelet coefficients at level 1 to identify the type of script. To reduce the dimension of the feature...
Face recognition is a quintessential biometric technique. It still remains challenging to accurately characterize the identity related features in face images. In this paper, we propose a novel classification method based on Kernel Fisher Discriminant Analysis using the distinctiveness of Gabor features and the robustness of ordinal measures. These parameters are derived from magnitude, phase, real...
Vascular networks in infrared faces are created due to the blood flow under the skin. Variations in blood flow in the blood vessels cause temperature difference, which produces the vascular networks. This paper deals with binary classification of various infrared facial expressions using vascular network. The classification has been performed using Support Vector Machine classifier on five types of...
Face recognition by computers in recent years has been a topic of intensive studies. In this problem, we witness several challenges: one has to cope with large data sets, solve problems of data extraction, and deal with poor quality of images caused by e.g., poor lighting of the subject. There have been a lot of algorithms and classifiers developed, which are aimed at recognizing faces of individuals...
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...
Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics experts. Due to tremendous success of deep learning in computer vision problems, there has been a lot of interest in applying features learned by convolutional neural...
The face recognition problem has been extensively studied by many researchers but accuracy is not satisfactory. This work presents analysis and performance evaluation of global methods (PCA, FLD, DCT, DWT), local methods (SIFT, LBP) and all possible fusions of two methods among them. The fusion is done by consolidating the output of multiple feature extraction algorithms at score levels using four...
Traffic congestion is a major concern in metropolitan areas and a quick congestion assessment of large-scale network is required for modern traffic management referred to as Intelligent Transport Systems (ITS). However, ITSs are facing the challenge of real-time storage, retrieval and processing of a vast amount of collected data over a large-scale network. Compressed sensing (CS) is an efficient...
In order to better reuse of motion capture data, complex motion sequences should be segmented into distinct behaviors. As we move toward collecting longer motion sequences, automatic behavior segmentation techniques are becoming important. In this paper, we proposed a method for automated segmentation motion capture data into distinct behaviors. We employ Gaussian Mixture Model (GMM) to model the...
In pattern recognition, the usage of appropriate similarity measure is crucial for acquiring robust performance. In this paper, we present two similarity measures, which compute a similarity between two matrices, based on the temporal feature variation and sequence length difference, respectively. Especially, the matrix object used in this paper has unique characteristic. Each row and column has different...
The Small Sample Size problem is a common issue in neuroimaging, where the number of features frequently surpass the number of samples, making generalization a difficult task and leading to inconsistent results. To overcome this problem, we have developed a new algorithm intended to generate a new set of functional images with similar characteristics to an original one, in our case, a subset of the...
The pattern recognition system for biometric identification, which was presented in this paper, used mathematical and statistical approaches such as Principal Component Analysis as a feature extraction method also Cross Validation and k-nearest neighbor with Euclidean metric distance for the classification method. The proposed recognition system used face and androgenic hair as biometric traits with...
This paper develops a Linear Discriminant Analysis based face recognition system in the Discrete Cosine Transform (DCT) domain as a departure from the traditional analysis in the spatial domain. In the training mode, the truncated DCT coefficients are used to find discriminating features for all the subjects in the image database. The compact representation of the truncated DCT coefficients leads...
Contingencies can impact security of power system operations. Severity of contingencies varies with pre-fault conditions. Therefore, it is of interest to use a database of analyzed pre-fault scenarios in order to recognize scenarios similar to the current, thus eliminating the need for continual and extensive on-line CSR, limiting this process only to faults identified as critical in similar cases...
Despite the success of Principal Component Analysis (PCA) for dimensionality reduction, it is known that its most expressive components do not necessarily represent important discriminant features for pattern recognition. In this paper, the problem of ranking PCA components, computed from multi-class databases, is addressed by building multiple linear learners that are combined through the AdaBoost...
The major issues that involve in identifying palm print are the search for the templates in the palm print database that best matches with the test sample from input. Here the fundamental to be solved are to select similar features of palm that are needs to be matched. The different feature of palm print that are able to discriminate them from each other must show a huge divergence between different...
Face spoofing can be performed in a variety of ways such as replay attack, print attack, and mask attack to deceive an automated recognition algorithm. To mitigate the effect of spoofing attempts, face anti-spoofing approaches aim to distinguish between genuine samples and spoofed samples. The focus of this paper is to detect spoofing attempts via Haralick texture features. The proposed algorithm...
Although the importance of barriers and the export entrepreneurship have attracted the attention of empirical researchers, they are mainly focused on either barriers alone or merely on the export entrepreneurship. Further, research studies till date have not addressed the different barriers and its influence on the export entrepreneurship (in terms of internationalization speed). Therefore, the current...
Personalized HRTFs(Head-related Transfer Functions) can be synthesized through the corresponding anthropometric features by the linear or nonlinear mapping from anthropometric features to HRTFs in the database. Some methods for synthesizing personalized HRTFs had been proposed. However, they were hard to be applied in practical circumstances to some extent. In this paper, we use principle component...
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