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In this paper, we propose a method to classify K-pop dance based on motion data obtained from Kinect V2 for research of motion classification and development of anti-plagiarism system. To do this, 200-point dances of K-pop are acquired. Dance motions from 40 amateur dancers are acquired to construct a total of 400 data. The proposed classification method consists of three steps. First, we obtain 13...
Face recognition has been gaining popularity for long time in various fields of human computer interaction. Moreover face recognition technique is widely used for automatic biometric security control, document verification, criminal investigation etc. In this paper we propose a new approach of using PCA based face recognition method for human verification. PCA based method seems to be interested due...
Inflammatory Bowel Disease (IBD) is an autoimmune condition that is observed to be associated with major alterations in the gut microbiome taxonomic composition. Here we classify major changes in microbiome protein family abundances between healthy subjects and IBD patients. We use machine learning to analyze results obtained previously from computing relative abundance of ∼10,000 KEGG orthologous...
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...
An efficient face recognition system should recognize faces in different views and poses. The efficiency of a human face recognition system depends on the capability of face recognition in presence of changes in the appearance of face due to expression, pose and illumination. A novel algorithm which utilizes the combination of texture and depth information based on Modular PCA to overcome the problem...
In this paper, a palmprint recognition using bitplane extraction with Principal Component Analysis is presented. Different distance measures are applied for classification to evaluate the recognition performance. Also, bit-plane is selected by analyzing the principal components. Hong Kong PolyU Palmprint Database is applied in this paper. The result showed that the palmprint recognition can achieved...
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...
During the last decades, several different techniques have been proposed for computer recognition of human faces. A further step in the development of these biometrics is to implement them in portable devices, such as mobile phones. Due to this devices' features and limitations it is necessary to select, among the currently available algorithms, the one with the best performance in terms of algorithm...
The paper deals with evaluation of automatic training samples selection method based on self-organizing map (SOM) in face recognition systems. In earlier paper [1] we presented an approach for automatic training samples selection using various clustering algorithms with good results on the CMU PIE face database. We showed that with the use of SOM we can achieve a good training samples selection. In...
To solve the challenging problem of face recognition under varying illumination conditions, we propose in this paper a novel LBP operator which we refer to as Local Binary Patterns with Circle Threshold (CT-LBP) operator. The CT-LBP operator can keep more discriminating information than the original LBP operator without losing the simplicity and effectivity of the original LBP operator. Extensive...
Face recognition is a biometric analysis tool that has enabled surveillance systems to detect humans and recognize humans without their co-operation. In this scheme face recognition is done by Principal Component Analysis (PCA). Face images are projected onto a face space that encodes best variation among known face images. The face space is defined by eigenface which are eigenvectors of the set of...
Face recognition has a major impact in security measures which makes it one of the most appealing areas to explore. To perform face recognition, researchers adopt mathematical calculations to develop automatic recognition systems. As a face recognition system has to perform over wide range of database, dimension reduction techniques become a prime requirement to reduce time and increase accuracy....
Modern scientific data consist of huge datasets which gathered by a very large number of techniques and stored in much diversified and often incompatible data repositories as data of bioinformatics, geoinformatics, astroinformatics and Scientific World Wide Web. At the other hand, lack of reference data is very often responsible for poor performance of learning where one of the key problems in supervised...
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...
In order to carry on the gait recognition fast and efficiently, a new representation scheme for feature description is proposed in this paper which utilizes nostationarity in the distribution of feature relationships. Firstly, edge pixels of silhouette are considered as low level features, then relationships among those features are characterized by two attributes, which are label of relative direction...
Face recognition is one of the most successful applications of image analysis and understanding and has gained much attention in recent years. This paper presents comparative analysis of two most popular appearance-based face recognition methods PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). It is generally believed that algorithms based on LDA are superior to those based...
This paper consists of development of detection strategies for face recognition tasks and to access its feasibility for forensic analysis using the FERET face database Author has used global feature extraction technique using statistical method for image classification. Facial images of three subjects with different expression and angles are used for classification. Principal Component Analysis has...
This paper presents a novel scheme for feature extraction for face recognition by fusing local and global discriminant features. The facial changes due to variations of pose, illumination, expression, etc. are often appeared only some regions of the whole face image. Therefore, global features extracted from the whole image fail to cope with these variations. To address these problems, face images...
The objective of the current work is to develop an automatic tool to identify microbiological data types using computer vision and pattern recognition. Current systems rely on the subjective reading of profiles by a human expert. This process is time-consuming and prone to errors. Bacteriophage (phage) typing & Fluorescent imaging methods are used to extract representative feature profiles and...
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