Data Augmentation-Assisted Makeup-Invariant Face Recognition

Dataset Augmentation for Pose and Lighting Invariant Face Recognition

The performance of modern face recognition systems is a function of the dataset on which they are trained. Most datasets are largely biased toward “near-frontal” views with benign lighting conditions, negatively effecting recognition performance on images that do not meet these criteria. The proposed approach demonstrates how a baseline training set can be augmented to increase pose and lightin...

A weakly supervised method for makeup-invariant face verification

Face verification, which aims to determine whether two face images belong to the same identity, is an important task in multimedia area. Face verification becomes more challenging when the person is wearing makeup. However, collecting sufficient makeup and non-makeup image pairs are tedious, which brings great challenges for deep learning methods of face verification. In this paper, we propose ...

Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification

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 (...

Background Invariant Face Recognition

As a contribution to handling the symbol grounding problem in AI an object recognition system is presented that is exempliied with human faces. It diiers from earlier systems by a pyramidal representation and the ability to cope with structured background.

Illumination Invariant Face Recognition