Current Automatic Palmprint Identification Systems (APIS) closely follow the matching philosophy of Automatic Fingerprint Identification Systems (AFIS), in that they exclusively use a small subset of Level II palmar detail, when matching a latent to an exemplar palm print. However, due the increased size and the significantly more complex structure of the palm, it has long been recognised that there is much detail that remains underutilised. Forensic examiners routinely use this additional information when manually matching latents. The thesis develops novel automatic feature extraction and matching methods which exploit the underutilised Level I detail contained in the friction ridge flow. When applied to a data base of exemplars, the approach creates a ranked list of matches. It is shown that the matching success rate varied with latent size. For latents of diameter 38mm, 91:1% were ranked first and 95:6% of the matches were contained within the ranked top 10. The thesis presents improved orientation field extraction methods which are optimised for friction ridge flow and novel enhancement techniques, based upon the novel use of local circular statistics on palmar orientation fields. In combination, these techniques are shown to provide a more accurate orientation estimate than previous work. The novel feature extraction stages exploit the level sets of higher order local circular statistics, which naturally segment the palm into homogeneous regions representing Level I detail. These homogeneous regions, characterised by their spatial and circular features, are used to form a novel compact tree-like hierarchical representation of the Level I detail. Matching between the latent and an exemplar is performed between their respective tree-like hierarchical structures. The methods developed within the thesis are complementary to current APIS techniques.