Verwandte Artikel zu PCA-AdaBoost-LDA Face Recognition Algorithm: A Robust...
PCA-AdaBoost-LDA Face Recognition Algorithm: A Robust Pose, occlusion and Illumination Invariant Face Recognition Algorithm on Low-Resolution Images - Softcover
Inhaltsangabe
This book presents a new method to recognize faces with high accuracy for the above aspects. A method with 68 points landmark-based face estimation and image normalization with AdaBoost-LDA for poses and illumination invariant face recognition is proposed. A single training image per person is derived from number of training image samples using average intensity values to reduce memory and execution time. AdaBoost-LDA is used for extraction of feature and classic nearest centre classifier is used for feature classification. Proposed method has successfully handled the illumination conditions, pose variations, and occlusion in low resolution images. Experimental results illustrate the promising performance of presented approach over the current published approaches on LFW, AR and CMU Multi-PIE databases.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Neu kaufen
Diesen Artikel anzeigen
EUR 54,90
EUR 60,00 für den Versand von Deutschland nach USA
Versandziele, Kosten & DauerSuchergebnisse für PCA-AdaBoost-LDA Face Recognition Algorithm: A Robust...
Foto des Verkäufers
PCA-AdaBoost-LDA Face Recognition Algorithm
Verlag:
LAP LAMBERT Academic Publishing Mär 2020, 2020
ISBN 10: 6202513470
ISBN 13: 9786202513470
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Neuware -This book presents a new method to recognize faces with high accuracy for the above aspects. A method with 68 points landmark-based face estimation and image normalization with AdaBoost-LDA for poses and illumination invariant face recognition is proposed. A single training image per person is derived from number of training image samples using average intensity values to reduce memory and execution time. AdaBoost-LDA is used for extraction of feature and classic nearest centre classifier is used for feature classification. Proposed method has successfully handled the illumination conditions, pose variations, and occlusion in low resolution images. Experimental results illustrate the promising performance of presented approach over the current published approaches on LFW, AR and CMU Multi-PIE databases.Books on Demand GmbH, Überseering 33, 22297 Hamburg 96 pp. Englisch. Artikel-Nr. 9786202513470
Anzahl: 2 verfügbar