Abstract—Face recognition across poses is a fundamental challenge in face recognition. The appearance of the face undergoes drastic changes with changes in the pose angle of the face. The popular uniform texture-based descriptor, Linear Binary Pattern (LBP), loses its appeal as the underlying intensity information changes. During pose changes, crucial facial key points get concealed and henceforth the rate of face recognition falls. In this paper, we propose utilizing an LBP-based texture descriptor, the Logically Concatenated Cross Binary Pattern, for extracting contour information from face images. The produced feature descriptors show a superior performance in terms of computational complexity. In order to retain only discriminant features, a balanced approach, like Marginalized Elastic-Net Regularization with ε-dragging, is fixed into the regularization term of the linear regression model. Classification is realized using regression residuals in the least-squares sense. In order to make the proposed approach robust to poses, Procrustes analysis is utilized at the preprocessing step when pose variations are significant. A comparative investigation of the proposed approach is presented by utilizing the UMIST database, the AT&T database, and the Indian Movie Face Database.
Index Terms—Discriminative elastic-net regularization, logically concatenated cross binary pattern, marginalized elastic-net regularized linear regression, negative dragging linear regression.
Kumud Arora is with the Inderprastha Engineering College, Ghaziabad, India (e-mail: kumud.kundu@ipec.org.in).
Poonam Gar is with the Institute of Management and Technology, Ghaziabad, India (e-mail: pgarg@imt.edu).
Cite: Kumud Arora and Poonam Garg, "Fusion of Logically Concatenated Cross Binary Pattern and ε-Dragging Linear Regression for Face Classification across Poses," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 108-115, 2020.
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