Face detection using local maxima

Automatic human face detection in digital images with a complex environment is still an unsolved problem in computer vision and pattern recognition. It has several uses, such as human face recognition, content based image retrieval and model based video coding. We present an automatic human face detection system where several methods are tested and compared. The underlying principle of the system is to compare subimages of the image pyramid, spanned by the input image, with a set of 'nose-eye' templates. However this comparison is not done on the entire set of subimages of the image pyramid, but on a small subset, which is defined by the 'local maxima method'. False positives are found by using a set of non-face templates. The system is tested on two databases, each include over 1000 images.