This paper presents appearance based methods for face recognition using linear and nonlinear techniques. The linear algorithms used are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The two nonlinear methods used are the Kernel Principal Components Analysis (KPCA) and Kernel Fisher Analysis (KFA). The linear dimensional reduction projection methods encode pattern in...

This paper presents appearance based methods for face recognition using linear and nonlinear techniques. The linear algorithms used are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The two nonlinear methods used are the Kernel Principal Components Analysis (KPCA) and Kernel Fisher Analysis (KFA). The linear dimensional reduction projection methods encode pattern in...

This paper presents appearance based methods for face recognition using linear and nonlinear techniques. The linear algorithms used are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The two nonlinear methods used are the Kernel Principal Components Analysis (KPCA) and Kernel Fisher Analysis (KFA). The linear dimensional reduction projection methods encode pattern in...

This paper presents appearance based methods for face recognition using linear and nonlinear techniques. The linear algorithms used are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The two nonlinear methods used are the Kernel Principal Components Analysis (KPCA) and Kernel Fisher Analysis (KFA). The linear dimensional reduction projection methods encode pattern in...

Affective computing increasingly gains interest in human-robot interaction. Within this research area, a large part of the studies concentrates on facial expressions and variations in speech as modalities. These modalities are especially suited during an interaction, but they may not be sufficient in other situations such as recognition at distance. Reviewing psychological studies, shows that h...

A model is proposed that relates opportunity recognition to pattern recognition—the process through which individuals perceive emergent patterns among seemingly unrelated stimuli or events. This model suggests that because of their unique knowledge structures (e.g., prototypes, exemplars), specific persons perceive patterns among emerging changes in technology, markets, demographics, etc. that ...

Determining optimal subspace projections that can maintain task-relevant information in the data is an important problem in machine learning and pattern recognition. In this paper, we propose a nonparametric nonlinear subspace projection technique that maintains class separability maximally under the Shannon mutual information (MI) criterion. Employing kernel density estimates for nonparametric...

In achieving accurate results, current nonlinear elastic recovery applications of finite element (FE) analysis have become more complicated for sheet metal springback prediction. In this paper, an artificial neural network (ANN) was used to mimic the nonlinear elastic recovery and provides a generalized solution in the FE analysis. The nonlinear elastic recovery was processed through back-propa...

Online Chinese handwriting recognition has attracted much research attention recently due to its complexity, wide-spread applications and emerging market demands. This material serves as a guide for pattern recognition researchers who have limited or no background in this language. We provide a brief review of the nature of the problem and challenges of online Chinese handwritten character reco...