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Springer Human Face Recognition Third-Order Synthetic Neural Networks

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Description

Human Face Recognition Using Third-Order Synthetic Neural Networks explores the practical application of high-order synthetic neural network technology for transformation-invariant recognition of complex visual patterns. This Springer volume demonstrates how high-order networks require minimal training data and achieve rapid training times while delivering exceptionally high recognition rates on visual pattern tasks. The book addresses real-world problems involving grayscale patterns, moving beyond traditional binary approaches. Ideal for researchers, engineers and computer scientists working in artificial intelligence and machine learning, this technical resource investigates the viability of advanced neural network architectures for robust facial recognition systems that maintain accuracy across various image transformations and conditions.

Key Features

Third-order synthetic neural networks provide transformation-invariant recognition capabilities for complex facial patterns and visual data.

High-order network architecture requires significantly less training data than conventional approaches, enabling faster model development.

Addresses practical grayscale pattern recognition problems beyond simple binary classification systems.

Springer's authoritative technical publication includes rigorous research and methods for machine learning professionals.

Explores state-of-the-art neural network applications in computer vision and artificial intelligence.

Demonstrates high recognition rates on demanding facial recognition tasks using advanced mathematical frameworks.

Product Specifications

Format
paperback
Domain
Amazon UK
Publication Date
30 June 1997
Listed Since
14 July 2013

Barcode

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