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. © Springer-Verlag 2004.
CITATION STYLE
Zhou, D., & Yang, X. (2004). Feature fusion based face recognition using EFM. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3212, 643–650. https://doi.org/10.1007/978-3-540-30126-4_78
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