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This study considers the problem of in-depth document analysis. We propose a new document analysis method, named Multi-Dimensional Linear Discriminant Analysis (MDL-DA), which enables us to formulate an efficient class specific semantic representation of local information from a document with respect to term associations and spatial distributions. MDL-DA works by firstly partitioning each document...
Face recognition is the process of identification of a person by their facial images. This technique makes it possible to use the facial image of a person to authenticate him into a secure system. Face is the main part of human being to be distinguished from one another. Face recognition system mainly takes an image as an input and compares this image with a number of images stored in database to...
In this paper we present a comparative study of two well-known face recognition algorithms. The contribution of this work is to reveal the robustness of each FR algorithm with respect to various factors, such as variation in pose and low resolution of the images used for recognition. This evaluation is useful for practical applications where the types of the expected images are known. The two FR algorithms...
An efficient illumination invariant face recognition method based on two-stage two dimensional linear discriminant analysis (2S2DLDA) is presented in this paper. The proposed method uses a reflectance-illumination model (RI-Model) based on maximum filter to obtain illumination invariants of an image. Various combinations of two dimensional feature extraction techniques (PCA, 2DPCA family and 2DLDA...
The development of accurate and robust palmprint recognition algorithm is a critical issue in automatic palmprint recognition system. In this paper, we propose a palmprint recognition method based on a two-phase test sample sparse representation. In the first phase, a test sample is represented as a linear combination of all the training samples and m "nearest neighbors" are selected based...
This article presents the task of speaker identification in a closed group. It discusses main steps of the identification process ranging from the proper speech features to the classification methods and statistical signal processing. However, its main focus is on tuning the final system using KNN classification method by setting up the number of neighbors, and reducing the feature vector dimension...
This paper presents a novel and efficient face recognition technique based on Local Binary Pattern (LBP) with threshold for resolving traditional LBP's weakness of extracting global features. By setting a threshold to enhance the robustness to noise such as light and extract the global features of face preferably. Combining the local features by LBP with global features as the total features of the...
Head pose estimation remains a unique challenge for computer vision system due to identity variation, illumination changes, noise, etc. Previous statistical approaches like PCA, linear discriminative analysis (LDA) and machine learning methods, including SVM and Adaboost, cannot achieve both accuracy and robustness that well. In this paper, we propose to use Gabor feature based random forests as the...
Face recognition has great demands in human recognition and recently it becomes one of the most important research areas of biometrics. In this paper, we present a novel layered face recognition method based on Fisher's linear discriminant analysis. The basic aim is to decrease FAR by reducing the face dataset to small size by applying layered linear discriminant analysis. Although, the computational...
Dimensionality reduction and feature selection is an important aspect of electroencephalography based event related potential detection systems such as brain computer interfaces. In our study, a predefined sequence of letters was presented to subjects in a Rapid Serial Visual Presentation (RSVP) paradigm. EEG data were collected and analyzed offline. A linear discriminant analysis (LDA) classifier...
In this paper, we propose a novel method using gender information for achieving better performances of face recognition systems. Gender is one of the important factors for recognizing appearance of human faces and there are many studies on gender classifications such. However, the gender information is not actively applied in vision-based face recognition tasks, because we cannot find out human identity...
Wood classification is fairly important to the forestry industry and forest conservation. There have already been some microcomputer-assisted classification methods which accomplish the classification mainly with feature image analysis. But it is also necessary to find out a more effective method to optimize the classification process. So we propose to apply principal component analysis (PCA), 2-dimensional...
Neural activity is very important source for data mining and can be used as a control signal for brain-computer interfaces (BCIs). Particularly, Magnetic signals of neurons are enriched with information about the movement of different part of the body such as wrist movement. In this paper, we use MEG (Magneto encephalography) signals of two subjects recorded during wrist movement task in four directions...
In order for robots to be able to manipulate the proper objects, robots firstly need visual ability to precisely recognize and identify objects. One of the most basic problems with robot vision is that environments can change under various weather conditions (various illuminations). Furthermore, each object's category consists of many objects with various poses. In order to obtain the best performance...
This paper evaluates face recognition applied to the real-world application of Facebook. Because papers usually present results in terms of accuracy on constrained face datasets, it is difficult to assess how they would work on natural data in a real-world application. We present a method to automatically gather and extract face images from Facebook, resulting in over 60,000 faces datasets, we evaluate...
Though linear discriminant analysis (LDA) is popular in the field of feature extraction, they usually encounter two problems when applied to face images. The first problem is that the between-class and within-class scatter matrices of LDA cannot be evaluated accurately because their dimensions are usually much larger than the number of available image samples. The second problem is the small sample...
Dimensionality reduction is one of the important preprocessing steps to handle high-dimensional data. Linear discriminant analysis (LDA) is a classical and popular approach for this purpose. LDA finds an optimal linear transformation, which maximizes the ratio of the variance in the between-class distance to the variance in the within-class distance. On the other hand, in order to overcome the limitation...
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