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Dataset Augmentation for Pose and Lighting Invariant Face Recognition

Abstract · Apr 14, 2017 01:56 ·

landmarks pose face shape lighting facial morphable conditions cs-cv

Arxiv Abstract

  • Daniel Crispell
  • Octavian Biris
  • Nate Crosswhite
  • Jeffrey Byrne
  • Joseph L. Mundy

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.

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