In this paper, an approach to reduce the computation steps required by fast neural networks for the searching process is
presented. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided
into small in size sub-images and then each one is tested separately using a fast neural network. The operation of fast
neural networks based on applying cross correlation in the frequency domain between the input image and the weights of
the hidden neurons. Compared to conventional and fast neural networks, experimental results show that a speed up ratio
is achieved when applying this technique to locate human faces automatically in cluttered scenes. Furthermore, faster
face detection is obtained by using parallel processing techniques to test the resulting sub-images at the same time using
the same number of fast neural networks. In contrast to using only fast neural networks, the speed up ratio is increased
with the size of the input image when using fast neural networks and image decomposition. This is our new achievement
over our previous publications 1,2,7,9. Moreover, simulation results are increased more than those presented in our
previous publications.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.