Abstract
This paper presents a fusing feature Fisher classifier (F3C) approach for face recognition, which is robust to moderate changes of illumination, pose and facial expression. In the F3C framework, a face image is first divided into smaller sub-images and then the discrete cosine transform (DCT) technique is applied to the whole face image and some sub-images to extract facial holistic and local features. After concatenating these DCT based facial holistic and local features to a facial fusing feature vector, the enhanced Fisher linear discriminant model (EFM) is employed to obtain a low-dimensional facial feature vector with enhanced discrimination power. Experiments on ORL and Yale face databases show that the proposed approach is superior to traditional methods, such as Eigenfaces and Fisherfaces.
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© 2004 Springer-Verlag Berlin Heidelberg
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Zhou, D., Yang, X. (2004). Feature Fusion Based Face Recognition Using EFM. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_78
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DOI: https://doi.org/10.1007/978-3-540-30126-4_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23240-7
Online ISBN: 978-3-540-30126-4
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