Abstract
This paper presents a new approach to face recognition which uses a new local descriptor, called Weber Local Descriptor (WLD).To extract local information further, the idea of dividing faces into small regions was adopted. Feature histogram is extracted from every region and concatenated into a single feature vector to efficiently represent the face image. The recognition is performed using a nearest neighbor classifier in the computed feature space with Chi square as a dissimilarity measure. The experiments on ORL, FERET, Yale face database show that the proposed approach is not only better than holistic methods such as PCA, KPCA, 2DPCA but also superior to LBP. Meanwhile, it’s robust to pose, noise, facial expressions and lightings.
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Rui, T., Yang, Z., Liu, F., Jiang, S., Li, H. (2013). Block-Based Face Recognition Using WLD. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_76
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DOI: https://doi.org/10.1007/978-3-319-03731-8_76
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03730-1
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