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We present DooDB, a doodle database containing data from 100 users captured with a touch screen-enabled mobile device under realistic conditions following a systematic protocol. The database contains two corpora: 1) doodles and 2) pseudo-signatures, which are simplified finger-drawn versions of the handwritten signature. The dataset includes genuine samples and forgeries, produced under worst-case conditions, where attackers have visual access to the drawing process. Statistical and qualitative analyzes of the data are presented, comparing doodles and pseudo-signatures to handwritten signatures. Time variability, learning curves, and discriminative power of different features are also studied. Verification performance against forgeries is analyzed using state-of-the-art algorithms and benchmark results are provided.