Compared with traditional biometrics like fingerprints and iris recognition, human odor‐based identification has attracted significant attention due to its unique advantages, including noncontact operation and high concealability. However, its relatively low long‐term prediction accuracy (current state‐of‐the‐art recognition rates are ~20%) limits practical applications. To explore the long‐term stability of human odor, this study uses a self‐developed electronic nose (eNose) system and selects three body odor sources—oral, ear, and armpit—for recognition. The experiment includes two parts: first, a 1‐month baseline test using standard cross‐validation, which confirms the system’s capability to capture distinctive odor patterns, with single‐day rates remaining high (98%–100%). Second, and most critically, a long‐term prediction experiment employing a strict time‐based data‐splitting strategy—training exclusively on first‐day samples and testing on all subsequent days—to prevent data leakage and evaluate real‐world generalization. This rigorous evaluation shows that first‐day armpit odor can predict identity 1 month later with 80% accuracy (overall average 70%), and monthly stability remains at ~60%. These findings confirm human odor’s potential for cross‐month identification under methodologically sound validation, providing key theoretical support for developing noncontact, long‐term biometric authentication systems.
Wang et al. (Thu,) studied this question.