ABSTRACT:
This paper deals with the development of an automated face
recognition system as for personal identification and verification where the
recognition phase is implemented using neural network (recognition classifier)
on low resolution images. The proposed system contains two parts, preprocessing
and face classification. The preprocessing part converts original images into
blurry image using average filter and equalizes the histogram of those image (lighting
normalization). The bi-cubic interpolation function is applied onto equalized
image to get resized image. The resized image is actually low-resolution image
providing faster processing for training and testing. The preprocessed image
becomes the input to neural network classifier, which uses learning algorithm
to recognize the familiar faces. Neural network with multi-valued neurons
for
image recognition will be considered in the
paper.
Such a network with original architecture analyzes
the phases of the Fourier
spectral coefficients corresponding to the low
frequencies. Quickly converged learning algorithm and huge functionality of multi-valued neurons allow getting
100% successful recognition of the different classes of
images including the blurred and corrupted ones. Simulation results are presented on the example of face recognition.
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