ONOT: a High-Quality ICAO-compliant Synthetic Mugshot Dataset
This provides a privacy-compliant dataset for researchers working on face analysis in electronic travel documents, though it is incremental as it applies existing generative methods to a new domain.
The paper tackles the problem of privacy and bias in face datasets by introducing ONOT, a synthetic dataset of high-quality mugshot images compliant with ICAO standards, which is publicly released to aid research in morphing attack detection and face quality assessment.
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.