Makeup is widely used to improve facial attractiveness and is well accepted by the public. However, different makeup styles will result in significant facial appearance changes. It remains a challenging problem to match makeup and non-makeup face images. This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (...

Lensing galaxies, unlike other samples of galaxies, are selected based on their masses and mass profile, rather than their colors or surface brightnesses. Therefore, gravitational lensing allows us to select galaxies based on their mass rather than their light, even at high redshift, without the need for medium resolution spectroscopy. Unfortunately, lensing by galaxy mass objects produces lens...

Cosmetic makeup significantly influences facial perception. Because faces consist of similar physical structures, cosmetic makeup is typically used to highlight individual features, particularly those of the eyes (i.e., eye shadow) and mouth (i.e., lipstick). Though event-related potentials have been utilized to study various aspects of facial processing, the influence of cosmetics on specific ...

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...