Dynamic Computer Vision

Syed Zahidur Rashid

ISBN 10: 3639761588 / ISBN 13: 9783639761580

Editore: Scholars' Press Feb 2015, 2015

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Neuware -This book focuses primarily on the development of orthoface for recognition. Orthoface method transforms faces from image space to face space. It achieves a dimension reduction similar to that of eigenface. All of the classification methods applicable to eigenface method can be applied to the orthoface method without any further modification. It maximises the inter-class scattering, and minimizes the intra-class scattering, which has been the weakness of the conventional eigenface method. The project matrix of the training face from the orthoface method forms an upper-triangular matrix. Each training face has a different number of coefficients allowing better discrimination and classification. The classification technique improves recognition speed significantly. This book also focuses on pose-invariant face recognition. This part concentrates on the use of a 3D head model and texture mapping technique to derive new pose views from one or two existing views, which is realised through the use of a deformable 3D head model. Deformation is done via parameters extracted from various feature measurements. The facial texture is mapped onto the 3D surface using cylinder texture mapping.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 212 pp. Englisch.

Codice articolo 9783639761580

Dati bibliografici

• Titolo: Dynamic Computer Vision
• Editore: Scholars' Press Feb 2015
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 3639761588
• ISBN 13: 9783639761580
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 220 L x 150 A x 13 P (millimetri) 220 millimetri di larghezza per 150 millimetri di altezza per 13 millimetri di profondità
• Peso: 334 grammi

Descrizione articolo

Riassunto

This book focuses primarily on the development of orthoface for recognition. Orthoface method transforms faces from image space to face space. It achieves a dimension reduction similar to that of eigenface. All of the classification methods applicable to eigenface method can be applied to the orthoface method without any further modification. It maximises the inter-class scattering, and minimizes the intra-class scattering, which has been the weakness of the conventional eigenface method. The project matrix of the training face from the orthoface method forms an upper-triangular matrix. Each training face has a different number of coefficients allowing better discrimination and classification. The classification technique improves recognition speed significantly. This book also focuses on pose-invariant face recognition. This part concentrates on the use of a 3D head model and texture mapping technique to derive new pose views from one or two existing views, which is realised through the use of a deformable 3D head model. Deformation is done via parameters extracted from various feature measurements. The facial texture is mapped onto the 3D surface using cylinder texture mapping.