Low‐resolution face alignment and recognition using mixed‐resolution classifiers

Abstract

A very common case for law enforcement is recognition of suspects from a long distance or in a crowd. This is an important application for low‐resolution face recognition (in the authors' case, face region below 40 × 40 pixels in size). Normally, high‐resolution images of the suspects are used as references, which will lead to a resolution mismatch of the target and reference images since the target images are usually taken at a long distance and are of low resolution. Most existing methods that are designed to match high‐resolution images cannot handle low‐resolution probes well. In this study, they propose a novel method especially designed to compare low‐resolution images with high‐resolution ones, which is based on the log‐likelihood ratio (LLR). In addition, they demonstrate the difference in recognition performance between real low‐resolution images and images down‐sampled from high‐resolution ones. Misalignment is one of the most important issues in low‐resolution face recognition. Two approaches – matching‐score‐based registration and extended training of images with various alignments – are introduced to handle the alignment problem. Their experiments on real low‐resolution face databases show that their methods outperform the state‐of‐the‐art.