Anzeige
Erschienen in:
2015 | OriginalPaper | Buchkapitel
Illumination Invariant Face Recognition Using Principal Component Analysis – An Overview
verfasst von : Çağrı Kaymak, Rüya Sarıcı, Ayşegül Uçar
Erschienen in: Machine Vision and Mechatronics in Practice
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Illumination variation is a challenge problem at face recognition since a face image varies as illumination changes. In this paper, it is reviewed the illumination variation methods in the state-of-the-art such as the single scale retinex algorithm, the multi scale retinex algorithm, the gradientfaces based normalization method, the Tan and Triggs normalization method and the single scale weberfaces normalization method. The face recognition is performed by using Principal Component Analysis (PCA) in MATLAB environment. AR face database is used for evaluating the face recognition algorithm using PCA. The distance classifier called as Squared Euclidean is used. Experimental results are comparatively demonstrated.