A probabilistic hit-and-miss transform for face localization
Face localization is needed for any face processing procedure whose applications range from biometric identification to content-based image retrieval. It consists in giving the image coordinates of the face. In this paper we propose a probabilistic pattern matching procedure for face localization in greyscale images similar to the morphological hit-and-miss-transform (HMT), which we call probabilistic HMT (PHMT). This procedure is defined on the morphological multiscale fingerprints (MMF), which are image features extracted from the morphological erosion/dilation scale spaces. The face location is estimated as the maximum likelihood image window matching both erosive and dilative MMF models of the object. The MMF models are computed at a discrete set of scales. The MMF models may be built up from a small set of training face images and do not involve numerically sophisticated training algorithms. Training does not use non-face sample images. Therefore resampling is not needed for the construction of the MMF models. The experimental results on the NIST Mugshot Identification Database endorse our claims about the accuracy and robustness of the proposed procedure.
hit-and-miss transform face localization computer vision mathematical morphology morphological scale spaces