Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’walking patterns to be recognized.Existing research in this area has primarily focused on fe...Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’walking patterns to be recognized.Existing research in this area has primarily focused on feature analysis through the extraction of individual features,which captures most of the information but fails to capture subtle variations in gait dynamics.Therefore,a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced.The gait features extracted from body halves divided by anatomical planes on vertical,horizontal,and diagonal axes are grouped to form canonical gait covariates.Canonical Correlation Analysis is utilized to measure the strength of association between the canonical covariates of gait.Thus,gait assessment and identification are enhancedwhenmore semantic information is available through CCA-basedmulti-feature fusion.Hence,CarnegieMellon University’s 3D gait database,which contains 32 gait samples taken at different paces,is utilized in analyzing gait characteristics.The performance of Linear Discriminant Analysis,K-Nearest Neighbors,Naive Bayes,Artificial Neural Networks,and Support Vector Machines was improved by a 4%average when the CCA-utilized gait identification approachwas used.Asignificant maximumaccuracy rate of 97.8%was achieved throughCCA-based gait identification.Beyond that,the rate of false identifications and unrecognized gaits went down to half,demonstrating state-of-the-art for gait identification.展开更多
We explore an idea of transferring some classic measures of global dependence between random variables Χ1, Χ2, L, Χn into cumulative measures of dependence relative at any point?(χ1, χ2, L, χn)?in the sample spa...We explore an idea of transferring some classic measures of global dependence between random variables Χ1, Χ2, L, Χn into cumulative measures of dependence relative at any point?(χ1, χ2, L, χn)?in the sample space. It allows studying the behavior of these measures throughout the sample space, and better understanding and use of dependence. Some examples on popular copula distributions are also provided.展开更多
设有 k 个母体 G_1,G_2,…,G_k,F_i 为来自母体 G_i 的随机变量,P_i 为其概率密度,根据多元统计分析理论,可以求出母体内的协方差阵 W 和各母体间的协方差阵 B。当样本归属于不同的母体空间时,则会引起 W 和 B 的变化。若某一种归属能使...设有 k 个母体 G_1,G_2,…,G_k,F_i 为来自母体 G_i 的随机变量,P_i 为其概率密度,根据多元统计分析理论,可以求出母体内的协方差阵 W 和各母体间的协方差阵 B。当样本归属于不同的母体空间时,则会引起 W 和 B 的变化。若某一种归属能使 W^(-1)B 的度量达到极大,则认为这种归属达到最优,于是可用 W^(-1)B 的特征方程的根来度量 W^(-1)B。其所有根的和可以 tr(W^(-1)B)表示,tr(W^(-1)B)表示 W^(-1)B 的迹。利用最大迹的判别分析方法可以识别油气异常。文中给出判别准则及具体计算方法,并以一个试验区为例,选取构造、层厚度、层振幅、层频率和层速度等五个参数变量组成五元变量,进行方差分析、均值检验和评判,说明这种方法具有识别油气的能力。展开更多
基金supported by Istanbul University Scientific Research Project Department with IRP-51706 Project Number.
文摘Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’walking patterns to be recognized.Existing research in this area has primarily focused on feature analysis through the extraction of individual features,which captures most of the information but fails to capture subtle variations in gait dynamics.Therefore,a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced.The gait features extracted from body halves divided by anatomical planes on vertical,horizontal,and diagonal axes are grouped to form canonical gait covariates.Canonical Correlation Analysis is utilized to measure the strength of association between the canonical covariates of gait.Thus,gait assessment and identification are enhancedwhenmore semantic information is available through CCA-basedmulti-feature fusion.Hence,CarnegieMellon University’s 3D gait database,which contains 32 gait samples taken at different paces,is utilized in analyzing gait characteristics.The performance of Linear Discriminant Analysis,K-Nearest Neighbors,Naive Bayes,Artificial Neural Networks,and Support Vector Machines was improved by a 4%average when the CCA-utilized gait identification approachwas used.Asignificant maximumaccuracy rate of 97.8%was achieved throughCCA-based gait identification.Beyond that,the rate of false identifications and unrecognized gaits went down to half,demonstrating state-of-the-art for gait identification.
文摘We explore an idea of transferring some classic measures of global dependence between random variables Χ1, Χ2, L, Χn into cumulative measures of dependence relative at any point?(χ1, χ2, L, χn)?in the sample space. It allows studying the behavior of these measures throughout the sample space, and better understanding and use of dependence. Some examples on popular copula distributions are also provided.
