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. We propose two approaches to integrating the IPD with a particle filter tracker. We use the BioID, FERET, LFPW, and COFW public datasets as well as five manually annotated high-definition video sequences to quantitatively evaluate the algorithms' performance. The video data set contains four subjects, different types of backgrounds, blurring due to fast motion, and occlusions. All code and data are available.

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. Automatic morphological segmentation has shown to be a fast and efficient method for selecting image regions for the boosting detector and allowed an improvement in the automatic tracking of handball players.

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). High-stressor scenarios involved more difficult performance tasks, negative social feedback, and greater time pressure relative to low workload scenarios. Stress reactions were tracked using self-report ratings, salivary cortisol, and heart rate. Subjects also completed personality, mood, and alexithymia questionnaires. To bootstrap development of the OCR algorithm, we had a human observer, blind to stressor condition, identify the expressive elements of the face of people undergoing high- vs. low-stressor performance. Different sets of videos of subjects' faces during performance conditions were used for OCR algorithm training. Subjective ratings of stress, task difficulty, effort required, frustration, and negative mood were significantly increased during high-stressor performance bouts relative to low-stressor bouts (all p < 0.01). The OCR algorithm was refined to provide robust 3-d tracking of facial expressions during head movement. Movements of eyebrows and asymmetries in the mouth were extracted. These parameters are being used in a Hidden Markov model to identify high- and low-stressor conditions. Preliminary results suggest that an OCR algorithm using mouth and eyebrow regions has the potential to discriminate high- from low-stressor performance bouts in 75-88% of subjects. The validity of the workload paradigm to induce differential levels of stress in facial expressions was established. The paradigm also provided the basic stress-related facial expressions required to establish a prototypical OCR algorithm to detect such changes. Efforts are underway to further improve the OCR algorithm by adding facial touching and automating application of the deformable masks and OCR algorithms to video footage of the moving faces as a prelude to blind validation of the automated approach.