Dataset Augmentation for Pose and Lighting Invariant Face Recognition

Abstract

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 lighting variability using semi-synthetic images with simulated pose and lighting conditions. The semi-synthetic images are generated using a fast and robust 3-d shape estimation and rendering pipeline which includes the full head and background. Various methods of incorporating the semi-synthetic renderings into the training procedure of a state of the art deep neural network-based recognition system without modifying the structure of the network itself are investigated. Quantitative results are presented on the challenging IJB-A identification dataset using a state of the art recognition pipeline as a baseline.

Appeared in 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

Cited by in corpus (5)

• Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects
• A 3D GAN for Improved Large-pose Facial Recognition
• Robust Registration and Geometry Estimation from Unstructured Facial Scans
• 3D-Aided Data Augmentation for Robust Face Understanding
• On the use of automatically generated synthetic image datasets for benchmarking face recognition