Abstract: An eye/gaze tracking system (100) receives first and second image streams (DIMG1, DIMG2) in first and second processing lines (110; 120) respectively. The first processing line (110) has at least one first processor (P1, P11, P12) generating a first set of components of eye-specific data (p1LG, p1LP, p1RG, p1RP) for producing eye/gaze data (DE/G). The second processing line (120) has at least one second processor (P2, P21, P22) generating a second set of components of eye-specific data (p2LG, p2LP, p2RG, p2RP) for producing the eye/gaze data (DE/G). The eye/gaze data (DE/G) describe an eye position and/or a gaze point of the subject (U).

Abstract: The present invention relates to a method for nonlinear classification of high dimensional data by means of boosting, whereby a target class with significant intra-class variation is classified against a large background class, where the boosting algorithm produces a strong classifier, the strong classifier being a linear combination of weak classifiers. The present invention specifically teaches that weak classifiers classifiers h1, h2, that individually more often than not generate a positive on instances within the target class and a negative on instances outside of the target class, but that never generate a positive simultaneously on one and the same target instance, are categorized as a group of anti-correlated classifiers, and that the occurrence of anti-correlated classifiers from the same group will generate a negative.

Abstract: The present invention relates to a method for nonlinear classification of high dimensional data by means of boosting, whereby a target class with significant intra-class variation is classified against a large background class, where the boosting algorithm produces a strong classifier, the strong classifier being a linear combination of weak classifiers. The present invention specifically teaches that weak classifiers classifiers h1, h2, that individually more often than not generate a positive on instances within the target class and a negative on instances outside of the target class, but that never generate a positive simultaneously on one and the same target instance, are categorized as a group of anti-correlated classifiers, and that the occurrence of anti-correlated classifiers from the same group will generate a negative.