ONOT: a High-Quality ICAO-compliant Synthetic Mugshot Dataset
Nicolò Di Domenico Guido Borghi Annalisa Franco Davide Maltoni
Nowadays, state-of-the-art AI-based generative models represent a viable
solution to overcome privacy issues and biases in the collection of datasets
containing personal information, such as faces. Following this intuition, in
this paper we introduce ONOT, a synthetic dataset specifically focused on the
generation of high-quality faces in adherence to the requirements of the
ISO/IEC 39794-5 standards that, following the guidelines of the International
Civil Aviation Organization (ICAO), defines the interchange formats of face
images in electronic Machine-Readable Travel Documents (eMRTD). The strictly
controlled and varied mugshot images included in ONOT are useful in research
fields related to the analysis of face images in eMRTD, such as Morphing Attack
Detection and Face Quality Assessment. The dataset is publicly released, in
combination with the generation procedure details in order to improve the
reproducibility and enable future extensions.