Analyzing Facial Behavioral Features from Videos

Face analysis from videos can be approached using two different strategies, depending on whether the temporal information is used or not. The most straightforward strategy applies still image based techniques to some selected (or all) frames and then fuses the results over the sequence. In contrast, an emerging strategy consists of encoding both facial structure and dynamics through spatiotemporal representations. To gain insight into the usefulness of facial dynamics, this paper considers two baseline systems and compares static versus spatiotemporal approaches to face analysis from videos. The first approach is based only on static images and uses spatial Local Binary Pattern features as inputs to SVM classifiers, while the second baseline system combines facial appearance and motion through a spatiotemporal representation using Volume LBP features as inputs to SVM classifiers. Preliminary experiments on classifying face patterns into different categories based on gender, identity, age, and ethnicity point out very interesting findings on the role of facial dynamics in face analysis from videos.