This paper proposes a new approach for face tracking based on the individual tracking of KLT features. The face is initially detected using a face detection scheme, and KLT features are distributed along the face. Each feature is tracked individually, and the displacement of the center of the face is obtained using a Weighted Vector Median Filter (WVMF) of the individual displacements. The scale change is then computed based on the position of each feature w.r.t. the center of the face. The experimental results indicate that the proposed approach is fast and robust in the presence of partial occlusions.

This paper presents a new approach for voice activity detection (VAD) in videoconferencing applications based only on video information. In the proposed approach, a face detection scheme is applied to locate the face of the participant, and anthropometric measures are used to identify one patch below each eye, as well as a neighborhood around the mouth. The patches below the eyes are used to train a skin color model suited for that specific person, which is then used to identify non-skin pixels in the mouth neighborhood. The number of non-skin pixels is taken as an estimate of the mouth openness, and its evolution across time is explored for VAD.

The number of features that can be completed over an image is, for practical purposes, limitless. Unfortunately, the number of features that can be computed and exploited by most computer vision systems is considerably less. As a result, it is important to develop techniques for selecting features from very large data sets that include many irrelevant or redundant features. This work addresses the feature selection problem by proposing a three-step algorithm. The first step uses a variation of the well known Relief algorithm to remove irrelevance; the second step clusters features using K-means to remove redundancy; and the third step is a standard combinatorial feature selection algorithm. This three-step combination is shown to be more effective than standard feature selection algorithms for large data sets with lots of irrelevant and redundant features. It is also shown to he no worse than standard techniques for data sets that do not have these properties. Finally, we show a third experiment in which a data set with 4096 features is reduced to 5% of its original size with very little information loss.