A Robust Feature Extraction Methodology for Improved Face Detection Performance within a Complex Background

A novel methodology is presented in this paper for dealing with the problem of face detection within a complex background. The proposed approach integrates a robust feature extraction technique based on a specific method of eigenanalysis of the unique classes identified in the problem at hand, with neural network based classifiers. Such an eigenaiysis aims at identifying principal characteristics of the above mentioned uniquely identified classes. Each unknown image, in the testing phase, is then, analyzed through a sliding window raster scanning procedure to sliding windows identified, through a first stage neural classifier, as belonging to one of the unique classes previously mentioned. After such a sliding window labeling procedure it is reasonable for a second stage neural classifier to be applied to the testing image viewed as a sequence of such labeled sliding windows for obtaining a final decision about whether a face exists within the given test image or not. Although the proposed approach is a second stage procedure, it is obvious that its most critical phase is the first stage classification process, since, if good identification/ labeling accuracy could be then obtained, it would facilitate final classification stage a lot. Therefore, the experimental section of this paper is conducted with respect to analyzing face specific classes labeling accuracy at such a first classification stage.