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Rule-based face detection in frontal views

Face detection is a key problem in building automated systems that perform face recognition. A very attractive approach for face detection is based on multiresolution images (also known as mosaic images). Motivated by the simplicity of this approach, a rule-based face detection algorithm in frontal views is developed that extends the work of G. Yang and T.S. Huang (see Pattern Recognition, vol.27, no.1, p.53-63, 1994). The proposed algorithm has been applied to frontal views extracted from the European ACTS M2VTS database that contains the videosequences of 37 different persons. It has been found that the algorithm provides a correct facial candidate in all cases. However, the success rate of the detected facial features (e.g. eyebrows/eyes, nostrils/nose, and mouth) that validate the choice of a facial candidate is found to be 86.5% under the most strict evaluation conditions.