Abstract

The AdaBoost algorithm is highly computational consuming and degraded issues. To solve these problems, an improved fast-Adaboost face detection algorithm based on LAC (linear asymmetric classifier) is presented in this paper. The proposed algorithm updated sample weight by two steps. First, training the first weak classifiers of node classifier by traditional weight updating method; Second, when a certain number of weak classifiers sequence is generated, degradation problems become serious, then the weak classifier sequences which have already been acquired are applied to form strong classifier by LAC algorithm, and the FPR (false positive rate) of Negative samples were calculated by strong classifier, the sample weight will be affected by FPR. Repeat these steps until the training of all the node weak classifiers is completed, then the best node classifier is got. At last, by cascading all the best node classifiers, the face detector is acquired. The experiment results show that the method not only improves the training speed and precision, but also inhibits the degradation phenomenon, which greatly improve the face detection performance.

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