Fast Object Detection using MLP and FFT
Souheil Ben-Yacoub · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 1997
. We propose a new technique that speeds up significantly the time needed by a trained network (MLP in our case) to detect a face in a large image. We reformulate neural activities in the hidden layer of the MLP in terms of filter convolution enabling the use of Fourier transform for an efficient computation of the neural activities. A formal proof and a complexity analysis are presented. Finally, some examples illustrate the approach. 2 IDIAP--RR 97-11 1 Introduction Face detection is the fundamental step before the recognition or identification procedure. Its reliability and time-response have a major influence on the performance and usability of the whole face recognition system. The large variability of human faces causes major difficulties in the design of a model that could encompass all possible faces [2]. Appearance-based approaches as well as learning-based approaches seem to be better suited for such a task. A set of representative faces is necessary to find the implicit m...