35117974
Online signature verification based on null component analysis and principal component analysis
This paper describes a method for stroke-based online signature verification using null component analysis (NCA) and principal component analysis (PCA). After the segmentation and flexible matching of the signature, we extract stable segments from each reference signature in order that the segment sequences have the same length. The reference set of feature vectors are transformed and separated into null components (NCs) and principal components (PCs) by K-L transform. Online signature verification is a special two-category classification problem and there is not a single available forgery set in an actual system. Therefore, it is different from the typical application of PCA in pattern recognition that both NCA and PCA are used to respectively analyze stable and unstable components of genuine reference set. Experiments on a data set containing a total 1,410 signatures of 94 signers show that the NCA/PCA-based online signature verification method can achieve better results. The best result yields an equal error rate of 1.9%.
7181717
A Biased Support Vector Machine Approach to Web Filtering
Web filtering is an inductive process which automatically builds a filter by learning the description of user interest from a set of pre-assigned web pages, and uses the filter to assign unprocessed web pages. In web filtering, content similarity analysis is the core problem, the automatic-learning and relativity-analysis abilities of machine learning algorithms help solve the above problems and make ML useful in web filtering. While in practical applications, different filtering task implies different userinterest and thus implies different filtering result. This work studies how to adjust the web filtering results to be more fit for the user interest. The web filtering result are divided into three categories: relative pages, similar pages and homologous pages according to different user interest. A Biased Support Vector Machine (BSVM) algorithm, which imports a stimulant function, uses training examples distribution n+/n−− and a user-adaptable parameter k to deal imbalancedly different classes of the pre-assigned pages, is introduced to adjust the filtering result to be best fit for the user interest. Experiments show that BSVM can greatly improve the web filtering performance.
34644832
On-Line Signature Verification Based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis)
On-line signature verification is still an active topic in the field of biometrics. This paper proposes a novel method based on PCA (Principal Component Analysis) and MCA (Minor Component Analysis). Different from the application of PCA in other fields, both principal and minor components are used to signature verification, and MC plays a very important role. Comparing with DTW and the discriminance of Euclidean distance, the method based on PCA and MCA is better. With 1215 signatures contributed by 81 signers of which numbers of reference signatures, genuine signatures and forgeries (skilled) are 5 respectively, the EER is about 5%.
38250707
Minimizing Spatial Deformation Method for Online Signature Matching
Elastic matching is an important algorithm for online signature verification. Most of elastic matching algorithms are based on dynamic time warping(DTW). In this paper, we propose a new points matching algorithm for online signature verification. After resampling a signature with equidistant spacing, we describe the resampling point by its (3+1) demesional((3+1)D) information: 3D spatial information (x-axis, y-axis, time) and a feature vector. A soft-assign matrix and two special restrictions are introduced for recording the correspondence between two signatures. With a deterministic annealing framework, the mapping between two sets of (3+1)D signature points is achieved by minimizing the warping energy of the thin-plate spline(TPS). Experimental results demonstrate that the proposed algorithm outperforms the DTW-based matching algorithms.
3439449
Classification of functional data: A segmentation approach
We suggest a classification and feature extraction method on functional data where the predictor variables are curves. The method, called functional segment discriminant analysis (FSDA), combines the classical linear discriminant analysis and support vector machine. FSDA is particularly useful for irregular functional data, characterized by spatial heterogeneity and local patterns like spikes. FSDA not only reduces the computation and storage burden by using a fraction of the spectrum, but also identifies important predictors and extracts features. FSDA is highly flexible, easy to incorporate information from other data sources and/or prior knowledge from the investigators. We apply FSDA to two public domain data sets and discuss the understanding developed from the study.
38198465
Control of a 3-dimensional snake-like robot
Biological snakes' diverse locomotion modes and physiology make them supremely adapted for environment. The special structure of snakes and their unique movement offer them peculiar ability of climbing and moving even in some ill-conditioned environments such as on the marshland or in narrow tubes. In this paper, we first discuss the controllability of a 3-dimensional snake-like robot, and then show the controllability of the robot by computer simulation.
10205775
Kernel-based Global Folley-Sammon Discriminate Analysis
Kernel trick is a useful method dealing with the nonlinearity of patterns ' distribution, and widely used for non-linear pattern classification problem. Recently, many kernel-based Fisher discriminate methods were proposed, the features extracted by those methods are statistically uncorrelated, usually ineffectively when used in so called "small sample problem "(SSS), which exists in most FR tasks. To overcome this shortcoming, in this paper we present Kernel-based Global Folley-Sammon Discriminate Analysis (KGFS), it can be realized in three steps: firstly by combing kernel function, map data onto an implicit high-dimensional feature space, secondly construct orthogonal feature space there, finally calculate iterative global orthogonal discriminate vectors. Experiments have been done on ORL dataset. In terms of classification error rate performance, proposed kernel based method KGFS has a better performance than other three commonly used kernel-based methods, such as GDA, MGDAandKDDA.
18568161
Analysis of locomotion of a planetary rover on a slope
For improvement of planetary rover locomotion, a new type of tracked vehicle was designed for planetary exploration based on the concept of reconfigurability. By applying the planetary wheel principle, the system can give output in different forms under various constraint conditions. As a reconfigurable module, the vehicle can be integrated into a larger system using two basic configurations. As a stand-alone rover, it has two moving modes. The whole design was analyzed by conducting dynamics simulations and experiments.
17592710
Full-Space LDA With Evolutionary Selection for Face Recognition
Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they usually utilize all eigenvectors of null or range subspaces of within-class scatter matrix (Sw). However, experimental results testified that not all the eigenvectors in the full space of S w are positive to the classification performance, some of which might be negative. As far as we know, there have been no effective ways to determine which eigenvectors should be adopted. This paper proposes a new method EDA+Full-space LDA, which takes full advantage of the discriminative information of the null and range subspaces of Sw by selecting an optimal subset of eigenvectors. An estimation of distribution algorithm (EDA) is used to pursuit a subset of eigenvectors with significant discriminative information in full space of Sw middot EDA+Full-space LDA is tested on ORL face image database. Experimental results show that our method outperforms other LDA methods