Looking at People Using Partial Least Squares

Analysis of images involving humans is of significant interest in computer vision because problems such as detection, modeling, recognition, and tracking are fundamental to model interactions between people and understand highlevel activities. Visual information contained in images is generally represented using feature descriptors. Many general classes of descriptors have been proposed focusing on different characteristics of images. Therefore, if one considers only a single descriptor, one might ignore useful information for a given task, compromising performance. In this work we consider a rich set of image descriptors analyzed by a statistical technique known as Partial Least Squares (PLS). PLS is a class of methods for modeling relations between sets of observations by means of latent variables and it is used to project exemplars from a very high dimensional feature space onto a low dimensional subspace. We first propose a method based on PLS to detect humans. Then, a framework based on a one-against-all classification scheme using PLS regression is described for face recognition. Results obtained for human detection and face recognition outperform state-of-art methods. Keywords-human detection; face recognition; Partial Least Squares;