2017/09/10 by Evgeniya Ustinova, E. Ustinova, Ustinova, E. +3 · 1 citation
Computer Science · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #Face recognition and analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.1709.03196
openalex publication_date 2017/09/10 · arxiv created 2017/10/15 · arxiv updated 2017/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Face verification and recognition problems have seen rapid progress in recent years, however recognition from small size images remains a challenging task that is inherently intertwined with the task of face super-resolution. Tackling this problem using multiple frames is an attractive idea, yet requires solving the alignment problem that is also challenging for low-resolution faces. Here we present a holistic system for multi-frame recognition, alignment, and superresolution of faces. Our neural network architecture restores the central frame of each input sequence additionally taking into account a number of adjacent frames and making use of sub-pixel movements. We present our results using the popular dataset for video face recognition (YouTube Faces). We show a notable improvement of identification score compared to several baselines including the one based on single-image super-resolution.