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An ensemble of patch-based subspaces for makeup-robust face recognition

Abstract : Recent research has demonstrated the negative impact of makeup on automated face recognition. In this work, we introduce a patch-based ensemble learning method, which uses multiple subspaces generated by sampling patches from before-makeup and after-makeup face images, to address this problem. In the proposed scheme, each face image is tessellated into patches and each patch is represented by a set of feature descriptors, viz., Local Gradient Gabor Pattern (LGGP), Histogram of Gabor Ordinal Ratio Measures (HGORM) and Densely Sampled Local Binary Pattern (DS-LBP). Then, an improved Random Subspace Linear Discriminant Analysis (SRS-LDA) method is used to perform ensemble learning by sampling patches and constructing multiple common subspaces between before-makeup and after-makeup facial images. Finally, Collaborative-based and Sparse-based Representation Classifiers are used to compare feature vectors in this subspace and the resulting scores are combined via the sum-rule. The proposed face matching algorithm is evaluated on the YMU makeup dataset and is shown to achieve very good results. It outperforms other methods designed specifically for the makeup problem.
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Contributor : Antitza Dantcheva Connect in order to contact the contributor
Submitted on : Tuesday, July 23, 2019 - 2:05:17 PM
Last modification on : Friday, November 18, 2022 - 9:23:40 AM




Chen Cunjian, Antitza Dantcheva, Arun Ross. An ensemble of patch-based subspaces for makeup-robust face recognition. Information Fusion, 2015, 32, pp.80 - 92. ⟨10.1016/j.inffus.2015.09.005⟩. ⟨hal-01247886⟩



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