Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild
This addresses face recognition challenges in the wild for applications like surveillance or security, but it is incremental as it applies existing super-resolution methods to enhance existing recognition pipelines.
The paper tackled the problem of low-resolution facial images in uncontrolled environments by applying a state-of-the-art super-resolution algorithm, which significantly improved recognition rates in unsupervised face recognition on the Labeled Faces in the Wild dataset.
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.