Face recognition at a distance: low-resolution and alignment problems

Existing face recognition techniques are very successful in recognizing high-resolution facial images. However, their performance is not sufficient on low-resolution facial images. In this thesis we focus on dealing with a typical forensic scenario, where the gallery images are the high-resolution mug-shots in the police database and the probe images are the low-resolution surveillance quality facial images.

We proposed a novel method, mixed-resolution biometric comparison, which allows direct low-resolution to high-resolution comparison. The method is based on the likelihood ratio framework where in the derivation of the expression for the likelihood ratio, the combined statistics of the low- and high-resolution images is taken into account. Our experiments on surveillance quality images demonstrate that this method significantly outperforms the state-of-the-art.

In literature on low-resolution face recognition, what in some papers is considered as low-resolution, is still considered as high-resolution in other papers. To harmonize the terminology in low-resolution face recognition, we propose a resolution scale. We define the range of low-resolution and further divide it into Upper Low Resolution, Moderately Low Resolution and Very Low Resolution.

Because the lack of low-resolution images, most of the existing low-resolution face recognition methods are trained and tested using down-sampled images. In this thesis, we test various face recognition methods and demonstrate that down-sampled images are not fully representative of realistic low-resolution images. We further demonstrate that, inaccurate alignment is the major problem that causes the poor recognition performance on real low-resolution images. In addition, we propose to use matching-score based registration to achieve better alignment and hence better face recognition performance.

In conclusion, we propose solutions to compare low-resolution probes with high-resolution galleries which significantly outperform the state-of-the-art on surveillance quality facial images. We emphasise that realistic low-resolution material should be used for training and testing. We focus attention on developing face recognition methods that can actually be useful for real-life applications. We bring an important step forward of low-resolution face recognition for forensic search.