Title :
Fast modular neural nets for face detection
Author :
El-Bakry, H.M. ; Abo-Elsoud, M.A. ; Kamel, M.S.
Author_Institution :
Fac. of Comput. Sci. & Inf. Syst., Mansoura Univ., Egypt
Abstract :
In this paper, a new approach to reduce the computation time taken by neural nets for the searching process is introduced. We combine both fast and cooperative modular neural nets to enhance the performance of the detection process. Such an approach is applied to identify human faces automatically in cluttered scenes. In the detection phase, neural nets are used to test whether a window of 20×20 pixels contains a face or not. The major difficulty in the learning process comes from the large database required for face/nonface images. A simple design for cooperative modular neural nets is presented to solve this problem by dividing these data into three groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. Simulation results for the proposed algorithm show a good performance
Keywords :
computational complexity; face recognition; neural nets; cluttered scenes; computation time; computational complexity; cooperative neural nets; detection process; face detection; modular neural nets; searching process; Computational complexity; Face detection; Face recognition; Humans; Image databases; Layout; Multi-layer neural network; Neural networks; Spatial databases; Testing;
Conference_Titel :
Circuits and Systems, 2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on
Conference_Location :
Geneva
Print_ISBN :
0-7803-5482-6
DOI :
10.1109/ISCAS.2000.857051