Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild

Majority of the face recognition algorithms use query faces captured from uncontrolled, in the wild, environment. Often caused by the cameras limited capabilities, it is common for these captured facial images to be blurred or low resolution. Super resolution algorithms are therefore crucial in improving the resolution of such images especially when the image size is small requiring enlargement. This paper aims to demonstrate the effect of one of the state-of-the-art algorithms in the field of image super resolution. To demonstrate the functionality of the algorithm, various before and after 3D face alignment cases are provided using the images from the Labeled Faces in the Wild (lfw). Resulting images are subject to testing on a closed set face recognition protocol using unsupervised algorithms with high dimension extracted features. The inclusion of super resolution algorithm resulted in significant improved recognition rate over recently reported results obtained from unsupervised algorithms

Super Resolution and 3D Alignment Effects on Unsupervised Face Recognition in the Wild

Majority of face recognition algorithms use query faces captured from uncontrolled, in the wild, environment. It is common for these captured facial images to be blurred or low resolution. Super resolution algorithms are therefore crucial in improving the resolution of such images especially when the image size is small requiring enlargement. This paper aims to demonstrate the effect of one of the state-of-the-art algorithms in the field of image super resolution. To demonstrate the functionality of the algorithm, various before and after 3D face alignment cases are provided using the images from the Labeled Faces in the Wild (lfw). Resulting images are subjected for testing on a closed set face recognition protocol using unsupervised algorithms with high dimension extracted features

Unsupervised Training for 3D Morphable Model Regression

We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed on-the-fly by rendering the predicted faces with a differentiable renderer. To make training from features feasible and avoid network fooling effects, we introduce three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.Comment: CVPR 2018 version with supplemental material (http://openaccess.thecvf.com/content_cvpr_2018/html/Genova_Unsupervised_Training_for_CVPR_2018_paper.html

Face Recognition - Algorithmic Approach For Large Datasets And 3D Based Point Clouds

This work proposes solutions for two different scenarios in face recognition and verification. The first scenario involves large scale unconstrained unsupervised face recognition. The proposed system for this scenario is a complete face recognition framework. The proposed system first studies the performance of unsupervised face recognition for frontalized captured faces in the wild under the effect of a single image super-resolution algorithm. The system also introduces new high dimensional features based on LBP and SURF that perform better than the state-of-the-art features for unconstrained unsupervised face recognition. To solve the large scale recognition process, a new algorithm has been designed to manipulate face images in the dataset. This new algorithm represents all training face images as a fully connected graph. The algorithm then divides the fully connected graph into simpler sub-graphs to enhance the overall recognition rate. The sub-graphs are generated dynamically, and a comparison between different sub-graph selection techniques including minimizing edge weight sums, random selection, and maximizing sum of edge weights inside the sub-graph is provided. Results show that the optimized hierarchical dynamic technique developed with sub-graphs selection increases the recognition rate in large benchmark image dataset by more than 40% for rank 1 recognition rate compared to the original single large graph method. The approach developed in this research is tested on different datasets, especially if the number of images per person in the training data is low. Furthermore, in order to improve rank 1 recognition rates and to reduce the computation time of the recognition process, a new technique that combines the hierarchical face recognition algorithm and a deep learning neural network using Siamese structure for face verification is proposed. The second part of this work addresses the usage of neural generative models for 3D faces with an application in face recognition when 3D datasets are utilized separately without the existence of texture information scenarios. An improved technique is developed to construct new representations for point clouds containing 3D information. The technique employs a regression neural network model trained using Levenberg-Marquardt (LM) algorithm. One of the advantages of this new representation is the significant reduction in storage space required for point clouds due to the utilization of a regression model for depth map regeneration. Moreover, the trained neural models can be used to generate a super-resolution version of the original 3D point clouds. The proposed regression representation is also used with a deep Siamese neural system to implement a complete depth-based neural face recognition and verification framework. The results indicate that the proposed system provides highly accurate and efficient face recognition results with 3D information only without texture information