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.