Adaptive discriminant wavelet features for statistical object detection

We present an adaptive feature selection scheme to jointly optimize the detector performance and the computational efficiency for statistical object detection. From the statistical distribution of wavelet coefficients, we construct an error-bound-tree (EBT) to analyze the error probability of the Bayes test. The wavelet features put into test are adaptively selected to minimize the detection error. The selected features are more discriminative than others and allow the detector to reach a decision faster without jeopardizing its accuracy. The proposed scheme is demonstrated in face detection.