Human Face Recognition Using Third-Order Synthetic Neural Networks
explores the viability of the application of High-order synthetic
neural network technology to transformation-invariant recognition
of complex visual patterns. High-order networks require little
training data (hence, short training times) and have been used to
perform transformation-invariant recognition of relatively simple
visual patterns, achieving very high recognition rates. The
successful results of these methods provided inspiration to address
more practical problems which have grayscale as opposed to binary
patterns (e.g., alphanumeric characters, aircraft silhouettes) and
are also more complex in nature as opposed to purely edge-extracted
images - human face recognition is such a problem. Human Face
Recognition Using Third-Order Synthetic Neural Networks serves as
an excellent reference for researchers and professionals working on
applying neural network technology to the recognition of complex
visual patterns.
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