Fast face detection via morphology-based pre-processing ?
An efficient face detection algorithm which can detect multiple faces oriented in any directions in a cluttered environment is proposed. In this paper, a morphology-based technique is first devised to perform eye-analogue segmentation. Next, the previously located eye-analogue segments are used as guides to search for potential face regions. Then, each of these potential face images is normalized to a standard size and fed into a trained backpropagation neural network for identification. In this detection system, the morphology-based eye-analogue segmentation process is able to reduce the background part of a cluttered image by up to 95%. This process significantly speeds up the subsequent face detection procedure because only 510% of the regions of the original image remain for further processing. Experiments demonstrate that an approximately 94% success rate is reached, and that the relative false detection rate is very low.
face detection backpropagation neural network morphological opening/closing operation