Weak Classifier for Density Estimation in Eye Localization and Tracking.

In this paper, we propose a fast weak classifier that can detect and track eyes in video sequences. The approach relies on a least-squares detector based on the inner product detector (IPD) that can stimate a probability density distribution for a feature's location-which fits naturally with a Bayesian estimation cycle, such as a Kalman or particle filter. As a least-squares sliding window detector, it possesses tolerance to small variations in the desired pattern while maintaining good generalization capabilities and computational efficiency.

Multiclass from binary: expanding one-versus-all, one-versus-one and ECOC-based approaches.

Recently, there has been a lot of success in the development of effective binary classifiers. Although many statistical classification techniques have natural multiclass extensions, some, such as the support vector machines, do not. The existing techniques for mapping multiclass problems onto a set of simpler binary classification problems run into serious efficiency problems when there are hundreds or even thousands of classes, and these are the scenarios where this paper's contributions shine.

Measuring handball players trajectories using an automatically trained boosting algorithm.

The aim of the present paper is to propose and evaluate an automatically trained cascaded boosting detector algorithm based on morphological segmentation for tracking handball players. The proposed method was able to detect correctly 84% of players when applied to the second period of that same game used for training and 74% when applied to a different game. Furthermore, the analysis of the automatic training using boosting detector revealed general results such as the training time initially increased with the number of figures used, but as more figures were added, the training time decreased.

Optical computer recognition of facial expressions associated with stress induced by performance demands.

Application of computer vision to track changes in human facial expressions during long-duration spaceflight may be a useful way to unobtrusively detect the presence of stress during critical operations. To develop such an approach, we applied optical computer recognition (OCR) algorithms for detecting facial changes during performance while people experienced both low- and high-stressor performance demands. Workload and social feedback were used to vary performance stress in 60 healthy adults (29 men, 31 women; mean age 30 yr).