Automatic feature extraction in latent fingerprints is a challenging problem due to poor quality of most latents, such as unclear ridge structures, overlapped lines and letters, and overlapped fingerprints. We proposed a latent fingerprint enhancement algorithm which requires manually marked region of interest (ROI) and singular points. The core of the proposed enhancement algorithm is a novel orientation field estimation algorithm, which fits orientation field model to coarse orientation field estimated from skeleton outputted by a commercial fingerprint SDK. Experimental results on NIST SD27 latent fingerprint database indicate that by incorporating the proposed enhancement algorithm, the matching accuracy of the commercial matcher was significantly improved.
The increase in twin births has created a requirement for biometric systems to accurately determine the identity
of a person who has an identical twin. The discriminability of some of the identical twin biometric traits,
such as fingerprints, iris, and palmprints, is supported by anatomy and the formation process of the biometric
characteristic, which state they are different even in identical twins due to a number of random factors during
the gestation period. For the first time, we collected multiple biometric traits (fingerprint, face, and iris) of
66 families of twins, and we performed unimodal and multimodal matching experiments to assess the ability
of biometric systems in distinguishing identical twins. Our experiments show that unimodal finger biometric
systems can distinguish two different persons who are not identical twins better than they can distinguish identical
twins; this difference is much larger in the face biometric system and it is not significant in the iris biometric
system. Multimodal biometric systems that combine different units of the same biometric modality (e.g. multiple
fingerprints or left and right irises.) show the best performance among all the unimodal and multimodal biometric
systems, achieving an almost perfect separation between genuine and impostor distributions.