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

作者：Chen Cunjian; Dantcheva Antitza; Ross Arun^*

来源：Information Fusion, 2016, 32: 80-92.

DOI：10.1016/j.inffus.2015.09.005

• Face recognition
• Makeup
• Cosmetics
• Ensemble learning
• Random subspace

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.

• 出版日期2016-11