Combining principal component techniques and psychological spaces to find perceptually similar faces

Generalization of affective learning about faces to perceptually similar faces.

Different individuals have different (and different-looking) significant others, friends, and foes. The objective of this study was to investigate whether these social face environments can shape individual face preferences. First, participants learned to associate faces with positive, neutral, or negative behaviors. Then, they evaluated morphs combining novel faces with the learned faces. The ...

Testing Principal Component Representations for Faces

A variety of experimental results indicate that the human visual system processes faces at least to some extent holistically, rather than by analysing individual features such as nose and eyes. Principal Components Analysis (PCA) of face images, which is widely used in engineering approaches to face identi cation, produces an inherently global representation. We investigate the psychological pl...

Nonlinear Principal Component Analysis and Related Techniques

Principal Component Analysis (PCA from now on) is a multivariate data analysis technique used for many different purposes and in many different contexts. PCA is the basis for low rank least squares approximation of a data matrix, for finding linear combinations with maximum or minimum variance, for fitting bilinear biplot models, for computing factor analysis approximations, and for studying re...

Principal component geodesics for planar shape spaces

In this paper a numerical method to compute principal component geodesics for Kendall’s planar shape spaces which are essentially complex projective spaces is presented. Underlying is the notion of principal component analysis based on geodesics for non-Euclidean manifolds as proposed in an earlier paper by Huckemann and Ziezold (2006). Currently, principal component analysis for shape spaces i...

Projection techniques for nonlinear principal component analysis