一种鲁棒且实时的人脸识别算法.ppt

A Robust Real Time Face Detection Outline AdaBoost – Learning Algorithm Face Detection in real life Using AdaBoost for Face Detection Improvements Demonstration AdaBoost A short Introduction to Boosting (Freund Schapire, 1999) Logistic Regression, AdaBoost and Bregman Distances (Collins, Schapire, Singer, 2002) Boosting The Horse-Racing Gambler Problem Rules of thumb for a set of races How should we choose the set of races in order to get the best rules of thumb? How should the rules be combined into a single highly accurate prediction rule? Boosting ! AdaBoost - the idea Initialize sample weights For each cycle: Find a classifier that performs well on the weighted sample Increase weights of misclassified examples Return a weighted list of classifiers AdaBoost - algorithm AdaBoost – training error Freund and Schapire (1997) proved that: AdaBoost ADApts to the error rates of the individual weak hypotheses. AdaBoost – generalization error Freund and Schapire (1997) showed that: AdaBoost – generalization error The analysis implies that boosting will overfit if run for too many rounds However, it was observed empirically that AdaBoost does not overfit, even when run thousands of rounds. Moreover, it was observed that the generalization error continues to drive down long after training error reached zero AdaBoost – generalization error An alternative analysis was presented by Schapire et al. (1998), that suits the empirical findings AdaBoost – different point of view We try to solve the problem of approximating the y’s using a linear combination of weak hypotheses In other words, we are interested in the problem of finding a vector of parameters α such that is a ‘good approximation’ of yi For classification problems we try to match the sign of f(xi) to yi AdaBoost – different point of view Sometimes it is advantageous to minimize some other (non-negative) loss function instead of the number of classification errors For AdaBoost the loss function is This