Cortical folding of the adult brain is highly convoluted and encodes inter-subject variable characteristics. Recent studies suggest that it is useful for individual identification in adults. However, little is known about whether the infant cortical folding, which undergoes dynamic postnatal development, can be used for individual identification. To fill this gap, we propose to explore cortical folding patterns for infant subject identification. This study thus aims to address two important questions in neuroscience: 1) whether the infant cortical folding is unique for individual identification; and 2) considering the region-specific inter-subject variability, which cortical regions are more distinct and reliable for infant identification. To this end, we propose a novel discriminative descriptor of regional cortical folding based on multi-scale analysis of curvature maps via spherical wavelets, called FoldingPrint. Experiments are carried out on a large longitudinal dataset with 1,141 MRI scans from 472 infants. Despite the dramatic development in the first two years, successful identification of 1-year-olds and 2-year-olds using their neonatal cortical folding (with accuracy >98%) indicates the effectiveness of the proposed method. Moreover, we reveal that regions with high identification accuracy and large inter-subject variability mainly distribute in high-order association cortices.