As we know, a robust approach to face and facial features detection must be able to handle the variation issues such as changes in imaging conditions, face appearances and image contents. Here we present a method which utilizes color, local symmetry and geometry information of human face based on various models. The algorithm first detects most likely face regions or ROIs (Region-Of-Interest) from the image using face color model and face outline model, produces a face color similarity map. Then it performs local symmetry detection within these ROIs to obtain a local symmetry similarity map. These two maps are fused to obtain potential facial feature points. Finally similarity matching is performed to identify faces between the fusion map and face geometry model under affine transformation. The output results are the detected faces with confidence values. Experimental results have demonstrated its validity and robustness to identify faces under certain variations.
展开▼
