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.
Type de document :
Article dans une revue
Information Fusion, Elsevier, 2015, 〈10.1016/j.inffus.2015.09.005〉
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Contributeur : Antitza Dantcheva <>
Soumis le : mardi 22 décembre 2015 - 23:47:35
Dernière modification le : jeudi 11 janvier 2018 - 15:51:43




Chen Cunjian, Antitza Dantcheva, Arun Ross. An ensemble of patch-based subspaces for makeup-robust face recognition. Information Fusion, Elsevier, 2015, 〈10.1016/j.inffus.2015.09.005〉. 〈hal-01247886〉



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