A Dynamic Approach to Pose Invariant Face Identification Using Cellular Simultaneous Recurrent Networks

Face recognition is a widely covered and desirable research field that produced multiple techniques and different approaches. Most of them have severe limitations with pose variations or face rotation. The immediate goal of this thesis is to deal with pose variations by implementing a face recognition system using a Cellular Simultaneous Recurrent Network (CSRN). The CSRN is a novel bio-inspired recurrent neural network that mimics reinforcement learning in the brain. The recognition task is defined as an identification problem on image sequences. The goal is to correctly match a set of unknown pose distorted probe face sequences with a set of known gallery sequences. This system comprises of a pre-processing stage for face and feature extraction and a recognition stage to perform the identification. The face detection algorithm is based on the scale-space method combined with facial structural knowledge. These steps include extraction of key landmark points and motion unit vectors that describe movement of face sequqnces. The identification process applies Eigenface and PCA and reduces each image to a pattern vector used as input for the CSRN. In the training phase the CSRN learns the temporal information contained in image sequences. In the testing phase the network predicts the output pattern and finds similarity with a test input pattern indicating a match or mismatch.Previous applications of a CSRN system in face recognition have shown promise. The first objective of this research is to evaluate those prior implementations of CSRN-based pose invariant face recognition in video images with large scale databases. The publicly available VidTIMIT Audio-Video face dataset provides all the sequences needed for this study. The second objective is to modify a few well know standard face recognition algorithms to handle pose invariant face recognition for appropriate benchmarking with the CSRN. The final objective is to further improve CSRN face recognition by introducing motion units which can be used to capture the direction and intensity of movement of feature points in a rotating fac

UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition

Recently proposed robust 3D face alignment methods establish either dense or sparse correspondence between a 3D face model and a 2D facial image. The use of these methods presents new challenges as well as opportunities for facial texture analysis. In particular, by sampling the image using the fitted model, a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map is always incomplete. In this paper, we propose a framework for training Deep Convolutional Neural Network (DCNN) to complete the facial UV map extracted from in-the-wild images. To this end, we first gather complete UV maps by fitting a 3D Morphable Model (3DMM) to various multiview image and video datasets, as well as leveraging on a new 3D dataset with over 3,000 identities. Second, we devise a meticulously designed architecture that combines local and global adversarial DCNNs to learn an identity-preserving facial UV completion model. We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance. Experiments on both controlled and in-the-wild UV datasets prove the effectiveness of our adversarial UV completion model. We achieve state-of-the-art verification accuracy, $94.05\%$, under the CFP frontal-profile protocol only by combining pose augmentation during training and pose discrepancy reduction during testing. We will release the first in-the-wild UV dataset (we refer as WildUV) that comprises of complete facial UV maps from 1,892 identities for research purposes