Abstract
This paper presents a novel approach for face recognition by boosting statistical local features based classifiers. The face image is scanned with a scalable sub-window from which the Local Binary Pattern (LBP) histograms [14] are obtained to describe the local features of a face image. The multi-class problem of face recognition is transformed into a two-class one by classifying every two face images as intra-personal or extra-personal ones [9]. The Chi square distance between corresponding Local Binary Pattern histograms of two face images is used as discriminative feature for intra/extra-personal classification. We use AdaBoost algorithm to learn a similarity of every face image pairs. The proposed method was tested on the FERET FA/FB image sets and yielded an exciting recognition rate of 97.9%.
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Zhang, G., Huang, X., Li, S.Z., Wang, Y., Wu, X. (2004). Boosting Local Binary Pattern (LBP)-Based Face Recognition. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds) Advances in Biometric Person Authentication. SINOBIOMETRICS 2004. Lecture Notes in Computer Science, vol 3338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30548-4_21
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DOI: https://doi.org/10.1007/978-3-540-30548-4_21
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