Articolo in rivista, 2020, ENG, 10.1016/j.imavis.2020.103927
Massoli F.V.; Amato G.; Falchi F.
CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy
Convolutional Neural Network models have reached extremely high performance on the Face Recognition task. Mostly used datasets, such as VGGFace2, focus on gender, pose, and age variations, in the attempt of balancing them to empower models to better generalize to unseen data. Nevertheless, image resolution variability is not usually discussed, which may lead to a resizing of 256 pixels. While specific datasets for very low-resolution faces have been proposed, less attention has been paid on the task of cross-resolution matching. Hence, the discrimination power of a neural network might seriously degrade in such a scenario. Surveillance systems and forensic applications are particularly susceptible to this problem since, in these cases, it is common that a low-resolution query has to be matched against higher-resolution galleries. Although it is always possible to either increase the resolution of the query image or to reduce the size of the gallery (less frequently), to the best of our knowledge, extensive experimentation of cross-resolution matching was missing in the recent deep learning-based literature. In the context of low- and cross-resolution Face Recognition, the contribution of our work is fourfold: i) we proposed a training procedure to fine-tune a state-of-the-art model to empower it to extract resolution-robust deep features; ii) we conducted an extensive test campaign by using high-resolution datasets (IJB-B and IJB-C) and surveillance-camera-quality datasets (QMUL-SurvFace, TinyFace, and SCface) showing the effectiveness of our algorithm to train a resolution-robust model; iii) even though our main focus was the cross-resolution Face Recognition, by using our training algorithm we also improved upon state-of-the-art model performances considering low-resolution matches; iv) we showed that our approach could be more effective concerning preprocessing faces with super-resolution techniques. The python code of the proposed method will be available at https://github.com/fvmassoli/cross-resolution-face-recognition.
Image and vision computing 99
Deep learning, Low resolution face recognition, Cross resolution face recognition
Massoli Fabio Valerio, Amato Giuseppe, Falchi Fabrizio
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 424525
Year: 2020
Type: Articolo in rivista
Creation: 2020-06-25 15:31:15.000
Last update: 2021-01-12 22:53:59.000
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1016/j.imavis.2020.103927
URL: https://www.sciencedirect.com/science/article/abs/pii/S0262885620300597
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:424525
DOI: 10.1016/j.imavis.2020.103927
Scopus: 2-s2.0-85085261425
ISI Web of Science (WOS): 000541130800003