CNN algorithms in face detection systems : a review
D. Bálya, Tamás Roska · 1998
A novel approach to the face detection problem is given, based on cellular neural networks (CNN) processing. The suggested CNN algorithms find and normalize human faces effectively while its time requirement is a fraction of the previously used methods. The algorithm starts with the detection of heads on color pictures using deviations in color and structure of the human face and that of the background. By normalizing the distance and position of the reference points, all faces could be transformed into the same size and position. For normalization, eyes serve as points of reference. The CNN algorithm finds the eyes on any grayscale image by searching some characteristic features of the eyes and eye sockets. Tests made on standard databases show that the algorithm works very fast and it is reliable. Illumination normalization can also be done by CNN algorithms. All results are embedded in a review of face detection and identification systems.