Abstract
A proof of concept was achieved in this project, in which the dimensions of a hyperspace were built, denoted “face hyperspace”, out of facial features. Each dimension in that space represents a facial feature or sub-feature. A vector (point) in that pace can represent a face. The vector is to some degree tolerant to lighting and contrast variations. For every feature, classifiers were built that process feature images to produce a number representing a feature class. To recognize a face image, it is passed through a face feature extraction routine that identifies and extracts each face feature in an image separately. Next each image feature is passed into a classifier, which in turn generates group numbers representing the face features. Finally, a vector representing the image is built by concatenating the classifier outputs of all features. The vector is compared to vectors in an image database for a matching vector using Euclidean distance.