This project has entered Phase I. 

Abstract

This project aims to develop accurate, robust and efficient 3D-aided face recognition algorithms from image and/or video for verification or identification in adverse outdoor conditions. Initial focus concentrated on the developing requirements for the software architecture of the face recognition system. After performing a comprehensive review of available datasets for face recognition, the project team curated two datasets for training and testing of algorithms suitable to work with images acquired by trail cameras. The project next sought to evaluate algorithms for landmark detection (input to pose estimation algorithm) and developed a new pose estimation algorithm based on deep learning. Next, the project team evaluated a variety of face recognition algorithms moving towards determining baseline estimates of performance. Investigation on the impact of global vs. local methods for face recognition and the effects of using multiple images also went into effect as part of the project. 

Presentations and Publications

Presentation | 2017 PI Meeting

Poster | 2017 BTI Institute Showcase

Article | Multi-view 3D face reconstruction with deep recurrent neural networks

Article | Monocular 3D facial shape reconstruction from a single 2D image with coupled-dictionary learning and sparse coding

Article | End-to-end 3D face reconstruction with deep neural networks

Article | UHDB31: A Dataset for Better Understanding Face Recognition across Pose and Illumination Variation

Article | Confidence driven network for point-to-set matching

Article | Annotated face model-based alignment: a robust landmark-free pose approach for 3D model registration

Article | GoDP: Globally Optimized Dual Pathway deep network architecture for facial landmark localization in-the-wild

Article | Facial 3D model registration under occlusions with sensiblepoints-based reinforced hypothesis refinement

Article | Evaluation of a 3D-aided pose invariant 2D face recognition system

Article | Patch-based face recognition using a hierarchical multi-label matcher

Article | Local Classifier Chains for Deep Face Recognition