Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions

In this paper, two new methods for online signature verification are proposed. The methods adopt the idea of the longest common subsequences (LCSS) algorithm to a kernel function for Support Vector Machines (SVM). The two kernels LCSS-global and LCSS-local offer the possibility to classify time series of different lengths with SVM. The similarity of two time series is determined very accurately since outliers are ignored. Consequently, LCSS-global and LCSS-local are more robust than algorithms based on dynamic time alignment such as Dynamic Time Warping (DTW). The new methods are compared to other kernel-based methods (DTW-kernel, Fisher-kernel, Gauss-kernel). Our experiments show that SVM with LCSS-local and LCSS-global authenticate persons very reliably.

The dynamic time warping (DTW) is state-of-the-art distance measure widely used in sequential pattern matching and it outperforms Euclidean distance in most cases because its matching is elastic and robust. It is tempting to substitute DTW distance for Euclidean distance in the Gaussian RBF kernel and plug it into the state-of-the art classifier support vector machines (SVMs) for sequence classification. However, it is not straightforward that DTW also outperforms Euclidean distance in kernel machines. While counter-examples can be found to numerically prove that DTW is not positive definite symmetric (PDS)acceptable by SVM, little is known why it can not be PDS theoretically. We analyze the DTW kernel and complete a theoretical proof via the connection between PDS kernel and reproducing kernel Hilbert space (RKHS). Our analysis leads to a better understanding that all Hilbertian metrics can be be converted to a PDS kernel in the Gaussian form, while the reverse is not true. The proof can be extended to conclude that elastic matching distance is not eligible to construct PDS kernels (e.g., Edit distance). Experiments were conducted to compare the RBF-kernel and DTW kernel in SVM classifications and the results show that simple Euclidean distance outperforms DTW in kernel machines.

Template matching methods using Dynamic Time Warping (DTW) have been used recently for online gesture recognition from body-worn motion sensors. However, DTW has been shown sensitive under the strong presence of noise in time series. In sensor readings, labeling temporal boundaries of daily gestures precisely is rarely achievable as they are often intertwined. Moreover, the variation in daily gesture execution always exists. Therefore, here we propose two template matching methods utilizing the Longest Common Subsequence (LCSS) to improve robustness against such noise for online gesture recognition. Segmented LCSS utilizes a sliding window to define the unknown boundaries of gestures in the continuous coming sensor readings and detects efficiently a possibly shorter gesture within it. WarpingLCSS is our novel variant of LCSS to determine occurrences of gestures without segmenting data and performs one order of magnitude faster than the Segmented LCSS. The WarpingLCSS requires low-resource settings to process new arriving samples, thus it is suitable for real-time gesture recognition implemented directly on the small wearable devices. We compare our methods with the existing template matching methods based on Dynamic Time Warping (DTW) on two real-world gesture datasets from arm-worn accelerometer data. The results demonstrate that the LCSS approaches outperform the existing template matching approaches (about 12% in accuracy) in the dataset that suffers from boundary noise and execution variation.

In keeping with recent advances of artificial intelligence and relevant technical developments, online signature verification demands lower calculation cost and higher security, and, systems that meet these criteria without deteriorating performance are needed. In response to the described need, this study proposes a novel, single-template strategy for a function-based approach to online signature verification. Specifically, to obtain an effective single template while preserving intra-user variability between all reference samples, we adopt dynamic time warping (DTW) barycenter averaging. Then, by using the mean template, we calculate both DTW from univariate time series and DTW from multivariate time series. After that, to boost the discriminative power, we apply a weighting scheme using random forests to efficiently combine the two types of DTW. The experimental results confirm the effectiveness of the proposed method for online signature verification using the popular SVC2004 Task2 dataset.

The similarity between a pair of time series, i.e., sequences of indexed values in time order, is often estimated by the dynamic time warping (DTW) distance, instead of any in the well-studied family of measures including the longest common subsequence (LCS) length and the edit distance. Although it may seem as if the DTW and the LCS(-like) measures are essentially different, we reveal that the DTW distance can be represented by the longest increasing subsequence (LIS) length of a sequence of integers, which is the LCS length between the integer sequence and itself sorted. For a given pair of time series of length n such that the dissimilarity between any elements is an integer between zero and c, we propose an integer sequence that represents any substring-substring DTW distance as its band-substring LIS length. The length of the produced integer sequence is O(c n^2), which can be translated to O(n^2) for constant dissimilarity functions. To demonstrate that techniques developed under the LCS(-like) measures are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.

The similarity between a pair of time series, i.e., sequences of indexed values in time order, is often estimated by the dynamic time warping (DTW) distance, instead of any in the well-studied family of measures including the longest common subsequence (LCS) length and the edit distance. Although it may seem as if the DTW and the LCS(-like) measures are essentially different, we reveal that the DTW distance can be represented by the longest increasing subsequence (LIS) length of a sequence of integers, which is the LCS length between the integer sequence and itself sorted. For a given pair of time series of n integers between zero and c, we propose an integer sequence that represents any substring-substring DTW distance as its band-substring LIS length. The length of the produced integer sequence is O(c⁴ n²) or O(c² n²) depending on the variant of the DTW distance used, both of which can be translated to O(n²) for constant cost functions. To demonstrate that techniques developed under the LCS(-like) measures are directly applicable to analysis of time series via our reduction of DTW to LIS, we present time-efficient algorithms for DTW-related problems utilizing the semi-local sequence comparison technique developed for LCS-related problems.

Dynamic time warping (DTW) is a state-of-the-art time series similarity measure method, which warps time axes to match the same shape between two time series with different lengths. However, its quadratic time and space complexity is an obstacle to its applications in the large time series data mining. To address this problem, some lower-bound functions for DTW, fast methods to approximately measure the distance between time series, are used to prune the dissimilar objects from time series database so as to retain the candidates for further measuring their similarity with DTW. So far, the existing lower-bound functions for DTW have been widely accepted for time series similarity search and indexing. In this paper, we propose the extensions of two existing lower-bound functions and discuss the relationships among them. The extensions are improved with high tightness and without much time cost. At the same time, we theoretically prove that these extensions satisfy lower-bound requirement and are better than their old versions respectively. The experimental results demonstrate that in most cases the quality of the proposed extensions of lower-bound functions for DTW outperforms the original versions except for a slightly higher time cost.

Signature verification is a widely and commonly accepted practice for authentication of an individual. Whereas off-line signature verification contributes very less to accurate identification, on-line signature verification has been successfully implemented in recent researches to achieve 80%-98% of accuracy. Various approaches have been used to implement biometric signature recognition some of which are dynamic time warping (DTW), Bayesian Learning, Hidden Markov model (HMM), Neural Networks, Support Vector machine (SVM) etc. This paper presents a comparative and qualitative study of these methods used for biometric signature verification.

Croplands are commonly mapped using time series of remotely sensed images. The dynamic time warping (DTW) algorithm is an effective method for realizing this. However, DTW algorithm faces the challenge of capturing complete and accurate representative cropland time series on a national scale, especially in Asian countries where climatic and topographic conditions, cropland types, and crop growth patterns vary significantly. This study proposes an automatic cropland extraction method based on the DTW algorithm and density-based spatial clustering of applications with noise (DBSCAN), hereinafter referred to as ACE-DTW, to map croplands in Pakistan in 2015. First, 422 frames of multispectral Landsat-8 satellite images were selected from the Google Earth Engine to construct monthly normalized difference vegetation index (NDVI) time series. Next, a total of 2409 training samples of six land cover types were generated randomly and explained visually using high-resolution remotely sensed images. Then, a multi-layer DBSCAN was used to classify NDVI time series of training samples into different categories automatically based on their pairwise DTW distances, and the mean NDVI time series of each category was used as the standard time series to represent the characteristics of that category. These standard time series attempted to represent cropland information and maximally distinguished croplands from other possible interference land cover types. Finally, image pixels were classified as cropland or non-cropland based on their DTW distances to the standard time series of the six land cover types. The overall cropland extraction accuracy of ACE-DTW was 89.7%, which exceeded those of other supervised classifiers (classification and regression trees: 78.2%; support vector machines: 78.8%) and existing global cropland datasets (Finer Resolution Observation and Monitoring of Global Land Cover: 87.1%; Global Food Security Support Analysis Data: 83.1%). Further, ACE-DTW could produce relatively complete time series of variable cropland types, and thereby provide a significant advantage in mountain regions with small, fragmented croplands and plain regions with large, high-density patches of croplands.

Optical image provides a unique spectral and textural opportunity to acquire data free of charge in cloud-free weather conditions. However, textural information from recent Sentinel-2A data has not been sufficiently recognized. Several researches have shown that texture features are essential for extracting crop information. This study presents a new scheme that integrates spectral bands and textural features for mapping maize fields and other land cover types (i.e., build-up, trees, others, and water) and evaluates the effectiveness of random forest (RF) and support vector machine (SVM) classifiers with different kernel functions based on Sentinel 2A (S-2A) data. The textural features were derived from multiple bands of S-2A image using a grey-level co-occurrence matrix (GLCM) algorithm. To normalize the analysis of these features, Random Forest feature selection was employed. The outcomes show that S-2A images are appropriate for mapping maize fields and other land covers when the image is accessible during the growing season. The SVM classifier with a radial basis function (RBF) kernel is superior to a polynomial (P), linear (L), and sigmoid (S) kernel functions, and the RF classifiers. For the SVM_RBF, the highest overall accuracy was obtained from the integration of spectral bands and textural features at scenario 2 SVM_RBF (87.40%), followed by the combination of spectral and textural features at scenario 2 SVM_P kernel (86.56%). However, scenario 5 SVM_P kernel (85.54%), scenario 4 SVM_P kernel function (85.30%), the spectral features alone, and the SVM_P kernel function, have the highest O/A (83.90%) than that of the SVM_RBF (83.66%). The inclusion of textural features contributed significantly in mapping the smallholder’s maize field in the study area. This demonstrated the value of S-2A in agricultural research.

Dynamic Time Warping (DTW) is a tried and tested online signature verification technique that still finds relevance in modern studies. However, DTW operates in a writer-dependent manner and its algorithm outputs unbounded distance values. The introduction of bounded outputs offers the prospect of cross pollination with other regression models which provide normalized outputs. Writer-dependent methods are heavily influenced by the richness of the available reference signature sets. Although writer-independent methods also use reference signatures, they have the ability to learn general characteristics of genuine and forged signatures. This ability particularly gives them an edge at detecting skilled forgeries. Noting that DTW, on the other hand, has a strength at random signature verification, this study proposes a model which combines DTW and Deep Neural Networks (DNNs). When trained on a class balanced training set from the BiosecurID dataset, using a best vs 1 reference signature selection scheme, the proposed hybrid model outperforms previous methods, achieving Equal Error Rates of 5.17 and 2.64 for skilled and random signature cases, respectively.

Beras Miskin (Raskin) Program is a program of social protection, as supporters of other programs such as nutrition improvement, healthy increase, education and productivity improvement of Poor Households. According to Badan Pusat Statistika, there were 14 criteria to determine a household is classified as poor households. Based on these criteria it will be classified of recipient households and non-recipient households of Beras Miskin (Raskin) Program by Support Vector Machine (SVM) method using LibSVM. The concept of classification by SVM is search for the best hyperplane which serves as a separator of two classes of data in the input space. Kernel function is used to convert the data into a higher dimensional space to allow a separation. LibSVM is a package program created by Chih-Chung Chang and Chih-Jen Lin from Department of Computer Science at National Taiwan University. The method used by LibSVM to obtain global solution of duality lagrange problem is decomposition method. To determine the best parameters of kernel function, used k-vold cross validation method and grid search algorithm. In this classification by SVM method using LibSVM, obtain the best accuracy value as 83,1933%, which is the kernel function Radial Basis Function (RBF). Keywords : Beras Miskin (Raskin) Program, Classification, Support Vector Machine (SVM), LibSVM, Kernel Function

In the field of machine learning, support vector machine is a supervised learning model which can analyze the data and identify patterns, and this theory is used for classification and regression analysis which is related to the learning algorithm.Support vector machine is a new machine learning method based on statistical learning theory, and has become a hot research topic in the machine learning field because of its excellent learning performance.Support vector machine is also a learning machine based on kernel function, and its generalization ability depends on the chosen kernel function to a great extent.Nevertheless, traditional support vector machine cannot achieve the desired results when training large-scale data set.Therefore, in order to improve training efficiency and generalization performance of support vector machine, we have to improve this algorithm.Although support vector machine has great advantages in theory, the research on its application is comparatively delayed.In this paper, we focus on novel support vector machine generated from multi-subjects amalgamation, such as fuzzy support vector machine, granular support vector machine, twin support vector machine.We analyze a large number of literature review about development trend of support vector machine and its applications in computer field, laying foundation for the systematic research on the correlation algorithm based on support vector machine.

Although most time-series data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for a single index structure that can support multiple distance measures. Our specific area of interest is the efficient retrieval and analysis of trajectory similarities. Trajectory datasets are very common in environmental applications, mobility experiments, video surveillance and are especially important for the discovery of certain biological patterns. Our primary similarity measure is based on the Longest Common Subsequence (LCSS) model, that offers enhanced robustness, particularly for noisy data, which are encountered very often in real world applications. However, our index is able to accommodate other distance measures as well, including the ubiquitous Euclidean distance, and the increasingly popular Dynamic Time Warping (DTW). While other researchers have advocated one or other of these similarity measures, a major contribution of our work is the ability to support all these measures without the need to restructure the index. Our framework guarantees no false dismissals and can also be tailored to provide much faster response time at the expense of slightly reduced precision/recall. The experimental results demonstrate that our index can help speed-up the computation of expensive similarity measures such as the LCSS and the DTW.

The maternal mortality rate during childbirth can be reduced through the efforts of the medical team in determining the childbirth process that must be undertaken immediately. Machine learning in terms of classifying childbirth can be a solution for the medical team in determining the childbirth process. One of the classification methods that can be used is the Support Vector Machine (SVM) method which is able to determine a hyperplane that will form a good decision boundary so that it is able to classify data appropriately. In SVM, there is a kernel function that is useful for solving non-linear classification cases by transforming data to a higher dimension. In this study, four kernel functions will be used; Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid in the classification process of childbirth in order to determine the kernel function that is capable of producing the highest accuracy value. Based on research that has been done, it is obtained that the accuracy value generated by SVM with linear kernel functions is higher than the other kernel functions.