Face Biometrics and Tracking

The natural way of recognizing a person is by a look in her/his face. Everybody is used to it, be it the observer or the observed. It is therefore obvious that biometrics need to include techniques to process images or videos containing faces since it is one of the most common tasks that we do with our vision routinely, and with an amazing accuracy.

Given images or live camera footage, the tasks of face biometrics are to detect faces and to recognize the person's identity, or at least to categorize it with regard to known/unknown,
age, gender - like a human would do. Then comes the task of tracking a person robustly in subsequent images. In analogy with human visual intelligence, the machines should be able to track with or without knowing the identity of the person. However, tracking means much more than this. It may also mean to track body parts accurately, e.g. eye, nose, mouth, and limbs. Accordingly, complex behaviors involving other objects can be recognized, for example, people carrying loads, danger of pedestrian-vehicle accidents.

The most significant challenges in face biometrics and tracking are probably robustness against illumination changes and cluttered background. We apply different features, including symmetry descriptors and Gabor filters. The latter demonstrably have analogies in the human visual cortex. We employ Support Vector Machines and Boosting techniques for pattern recognition. Examples of successful face localization in bad light conditions are on the left, feasible by our methods (benchmark YALE database). We are also pioneers to offer several anti-spoofing measures, able to avert advanced attacks by portable video devices.