Name: Pca-Adaboost-Lda Face Recognition Algorithm: A Robust Pose, Occlusion and Illumination Invariant Face Recognition Algorithm on Low-Resolution Images (en Inglés)
SKU: 52694663
Price: 80.74 USD
Availability: InStock
Author: Mahmood Ul Haq; Aamir Shahzad
ISBN: 9786202513470

portada Pca-Adaboost-Lda Face Recognition Algorithm: A Robust Pose, Occlusion and Illumination Invariant Face Recognition Algorithm on Low-Resolution Images (en Inglés)

Pca-Adaboost-Lda Face Recognition Algorithm: A Robust Pose, Occlusion and Illumination Invariant Face Recognition Algorithm on Low-Resolution Images (en Inglés)

Formato

Libro Físico

Editorial

Lap Lambert Academic Publishing

Autor

Mahmood Ul Haq; Aamir Shahzad

Año

2020

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9786202513470

Categorías

Ingeniería electrónica

Pca-Adaboost-Lda Face Recognition Algorithm: A Robust Pose, Occlusion and Illumination Invariant Face Recognition Algorithm on Low-Resolution Images (en Inglés)

Mahmood Ul Haq; Aamir Shahzad (Autor) · Lap Lambert Academic Publishing · Tapa Blanda

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Reseña del libro "Pca-Adaboost-Lda Face Recognition Algorithm: A Robust Pose, Occlusion and Illumination Invariant Face Recognition Algorithm on Low-Resolution Images (en Inglés)"

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.