Unsupervised face recognition in the wild using high-dimensional features under super-resolution and 3D alignment effect

Face recognition algorithms customarily utilize query faces captured from uncontrolled, in the wild, environments. The quality of these facial images is affected by various internal factors, including the quality of sensors used in outdoor cameras as well as external ones, such as the quality and direction of light. These factors adversely affect the overall quality of the captured images often causing blurring and/or low resolution, a phenomena commonly referred to as image degradation. Super-resolution algorithms are highly effective in improving the resolution of degraded images, more so if the captured face is small requiring scaling up. With this motivation, this research aims at demonstrating the effect of one of the state-of-the-art image super-resolution algorithms on the labeled faces in the wild (lfw) dataset. In this regard, several cases are analyzed to demonstrate the effectiveness of the super-resolution algorithm. Each case is then investigated independently comparing the order of execution before or after the 3D face alignment step. Following this, resulting images are tested on a closed set face recognition protocol using unsupervised algorithms with high-dimensional extracted features. The inclusion of super-resolution resulted in improvement in the recognition rate compared to unsupervised algorithm results reported in the literature.