Identity Verification from Human Scent using Channel Representation of 2D Gas Chromatography-Mass Spectrometry Data

This study examines the feasibility of employing raw two-dimensional gas chromatography/time-of-flight mass spectrometry (GCxGC ToF-MS) data for the purpose of human scent identity verification. Unlike techniques that require expert-driven identification of compounds, our framework transforms each GCxGC sample into a multi-channel image. A comprehensive assessment has been conducted on ten channel-encoding schemes, five spatial-alignment strategies, and ten feature-embedding methods.The evaluation is performed on a newly assembled dataset of 252 individuals, comprising 2,528 raw samples and aggregating around 7.5TB of data. In contrast to conventional methodologies employed in chemical analysis, our research demonstrates that alignment to a common spatial reference frame is unnecessary. The best performing method reaches an approximately 53% true positive rate at a 5% false positive rate. Although this performance is below that of well-established biometrics (e.g., iris verification), our results underscore the feasibility of raw-odor-based verification for scenarios where direct line-of-sight or cooperation may be limited, thereby revealing opportunities for interdisciplinary research.We will release the code and datasets with the camera ready.