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.
Reihe
Auflage
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Research
Produkt-Hinweis
Fadenheftung
Gewebe-Einband
Illustrationen
Maße
Höhe: 234 mm
Breite: 156 mm
Dicke: 10 mm
Gewicht
ISBN-13
978-0-7923-9957-5 (9780792399575)
DOI
10.1007/978-1-4615-4092-2
Schweitzer Klassifikation
1. Introduction.- 1.1 Objective.- 1.2 Background to Neural Networks.- 1.3 Organization of book.- 2. Face Recognition.- 2.1 Background.- 2.2 Various methods.- 2.3 Neural Net Approach.- 3. Implementation of Invariances.- 3.1 Matching of similar triplets.- 3.2 Software implementation.- 4. Simple Pattern Recognition.- 4.1 Procedure.- 4.2 Results.- 5. Facial Pattern Recognition.- 5.1 Two-dimensional moment invariants.- 5.2 Face Segmentation.- 5.3 Isodensity regions.- 5.4 Reducing sensitivity to lighting conditions.- 5.5 Image encoding algorithm.- 5.6 The use of gradient images.- 6. Network Training.- 6.1 Training algorithms.- 6.2 Modifications to training algorithms.- 6.3 Training image data.- 6.4 Results.- 7. Conclusions amp; Contributions 111.- 8. Future Work.- 8.1 Simultaneous Training on all four Isodensity Images.- 8.2 Higher-resolution coarse image size.- 8.3 Automatic face recognition.- 8.4 MIMO third-order networks.- 8.5 Zernike and Complex moments.- 8.6 Recognition of facial expressions (moods).- Index 119.
