ONOT: a High-Quality ICAO-compliant Synthetic Mugshot Dataset
2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)（2024）
Abstract
Nowadays, state-of-the-art AI-based generative models represent a viablesolution to overcome privacy issues and biases in the collection of datasetscontaining personal information, such as faces. Following this intuition, inthis paper we introduce ONOT, a synthetic dataset specifically focused on thegeneration of high-quality faces in adherence to the requirements of theISO/IEC 39794-5 standards that, following the guidelines of the InternationalCivil Aviation Organization (ICAO), defines the interchange formats of faceimages in electronic Machine-Readable Travel Documents (eMRTD). The strictlycontrolled and varied mugshot images included in ONOT are useful in researchfields related to the analysis of face images in eMRTD, such as Morphing AttackDetection and Face Quality Assessment. The dataset is publicly released, incombination with the generation procedure details in order to improve thereproducibility and enable future extensions.
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Key words
Privacy Issues,Face Images,Generation Procedure,Detail In Order,Collection Bias,International Civil Aviation Organization,Number Of Images,Large-scale Datasets,Face Recognition,Generative Adversarial Networks,Diffusion Model,Image Generation,Synthetic Images,Variational Autoencoder,Face Detection,Morphing,Freckles,Adjacency Matrix Of Graph,Generative Adversarial Networks Model,Real Identity,Compliance Testing,Face Recognition Model,Head Pose,Identity Preservation,Uniform Light,Facial Recognition Technology,Proportion Of Images,Facial Expressions,Label Noise,Need For Imaging
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