Modular construction of time-delay neural networks for speech recognition
Neural Computation
Concurrency: Practice and Experience
Handbook of image processing operators
Handbook of image processing operators
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human iris detection using fast cooperative modular neural nets and image decomposition
Machine Graphics & Vision International Journal
Fast information processing over business networks
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
Real-time detection of face and iris
WSEAS Transactions on Signal Processing
Fast human motion tracking by using high speed neural
SSIP '09/MIV'09 Proceedings of the 9th WSEAS international conference on signal, speech and image processing, and 9th WSEAS international conference on Multimedia, internet & video technologies
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
Surveillance of video signals over computer networks
ACC'11/MMACTEE'11 Proceedings of the 13th IASME/WSEAS international conference on Mathematical Methods and Computational Techniques in Electrical Engineering conference on Applied Computing
Real-time transmission of video streaming over computer networks
EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology
A new fast neural network model
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
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In this paper, a new approach to reduce the computation time taken by neural networks for the searching process is introduced. Both fast and cooperative modular neural networks are combined 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 networks 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 networks 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 on Bio database show a good performance.