Face Detection Based on Frequency Domain Features

In this paper we have developed a novel face detection method using the Stockwell and the log dyadic wavelet transform features, following the cascaded face detectors framework. Stockwell transform (ST) time frequency distribution of an image region is known for its excellent feature representational capabilities (due to the high resolution of the distribution). Log dyadic wavelet transform (LDWT) is capable of representing image patches with high accuracy. We have used the Stockwell transform and the log dyadic wavelet transform for representing the facial features effectively. Our face detection method consists of two stages. The first stage consists of a cascade of 4 face detectors constructed using discriminative facial ST features selected by the ADABOOST feature selection method. The second stage consists of a cascade of 4 more face detectors, each of them is a SVM classifier trained with face/nonfacial LDWT features. We have conducted our face detection experiments on the well known CMU-MIT and FDDB face detection datasets to verify the efficacy of our method.