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

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    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.
    Original languageEnglish
    Pages (from-to)418-428
    Number of pages11
    JournalIET biometrics
    Issue number6
    Early online date24 Apr 2017
    Publication statusPublished - Nov 2017


    • face recognition
    • statistical analysis
    • image registration
    • police data processing
    • image classification
    • low-resolution face alignment
    • mixed-resolution classifiers
    • law enforcement
    • suspect recognition
    • suspect high-resolution images
    • log-likelihood ratio
    • LLR
    • image recognition
    • matching-score-based registration
    • image extended training
    • low-resolution face databases
    • 2023 OA procedure


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