Fast objects detection by variancemaximization learning of lifting wavelet filters

A fast objects detecting method is proposed, which is based on the variance-maximization learning of lifting dyadic wavelet fil-ters. First, it is shown that the sum of lifting wavelet coefficients in horizontal and vertical directions defines an elliptic-type of dis-crete operator containing free parameters. The free parameters are learned so as to maximize the variance of lifting wavelet coeffi-cients for a target object. Since this problem is an ill-posed prob-lem, a regularization method is employed to solve it. Objects in a query image similar to the target object are detected by the use of the learned filter. Simulation concerns the detection of narrow eyes from faces. 1