A video database for facial behavior understanding

[EN]The use of new technologies in order to step up the inter- action between humans and machines is the main proof that faces are important in videos. Therefore we suggest a novel Face Video Database for development, testing and veri cation of algorithms related to face- based applications and to facial recognition applications. In addition of facial expression videos, the database includes body videos. The videos are taken by three di erent cameras, working in real time, without vary- ing illumination conditions.

Competition on counter measures to 2-D facial spoofing attacks

[EN]Spoofing identities using photographs is one of the most common techniques to attack 2-D face recognition systems. There seems to exist no comparative stud- ies of di erent techniques using the same protocols and data. The motivation behind this competition is to com- pare the performance of di erent state-of-the-art algo- rithms on the same database using a unique evaluation method. Six di erent teams from universities around the world have participated in the contest.

Combining face and facial feature detectors for face detection performance improvement

[EN]In this paper, we experimentally study the combination of face and facial feature detectors to improve face detection performance. The face detection problem, as suggeted by recent face detection challenges, is still not solved. Face detectors traditionally fail in large-scale problems and/or when the face is occluded or di erent head rotations are present. The combination of face and facial feature detectors is evaluated with a public database. The obtained results evidence an improvement in the positive detection rate while reducing the false detection rate. Additionally, we prove that the integration of facial feature detectors provides useful information for pose estimation and face alignment.

An evolutive approach for smile recognition in video sequences

Facial expression recognition is one of the most challenging research areas in the image recognition ¯eld and has been actively studied since the 70's. For instance, smile recognition has been studied due to the fact that it is considered an important facial expression in human communication, it is therefore likely useful for human–machine interaction. Moreover, if a smile can be detected and also its intensity estimated, it will raise the possibility of new applications in the future

An analysis of facial description in static images and video streams

[ES]This paper describes an analysis performed for facial description in static images and video streams. The still image context is first analyzed in order to decide the optimal classifier configuration for each problem: gender recognition, race classification, and glasses and moustache presence. These results are later applied to significant samples which are automatically extracted in real-time from video streams achieving promising results in the facial description of 70 individuals by means of gender, race and the presence of glasses and moustache.

An incremental learning algorithm for face recognition

[EN]In face recognition, where high-dimensional representation spaces are generally used, it is very important to take advantage of all the available information. In particular, many labelled facial images will be accumulated while the recognition system is functioning, and due to practical reasons some of them are often discarded. In this paper, we propose an algorithm for using this information. The algorithm has the fundamental characteristic of being incremental. On the other hand, the algorithm makes use of a combination of classification results for the images in the input sequence. Experiments with sequences obtained with a real person detection and tracking system allow us to analyze the performance of the algorithm, as well as its potential improvements.