Fast Modular Neural Nets for Human Face Detection

Authors:
H. M. El-Bakry
Affiliations:
-
Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Year:
2000

Citing 0
Cited 8

Fast Iris Detection for Personal Verification Using Modular Neural Nets

Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
A Rotation Invariant Algorithm for Recognition

Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Fast Face Detection Using Neural Networks and Image Decomposition

AMT '01 Proceedings of the 6th International Computer Science Conference on Active Media Technology
Speeding-up normalized neural networks for face/object detection

Machine Graphics & Vision International Journal
New fast normalized neural networks for pattern detection

Image and Vision Computing
Personal identification through biometric technology

AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
New high speed normalized neural networks for pattern detection

SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
A new expert system for pediatric respiratory diseases by using neural networks

AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory

Quantified Score

Hi-index	0.00

Visualization

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 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 20x20 pixels contains a face or not. The major difficulty in the learning process comes from the large database required for face/non-face 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.