This paper proposes new machine learning strategies for person identification which can be used in several biometric modalities such as friction ridges, handwriting, signatures, and speech.
The biometric or forensic performance task answers the question of whether a sample belongs to a known person. Two different learning paradigms are discussed: person-independent (or general learning) and person-dependent (or person-specific learning). In the first paradigm, learning is from a general population of ensemble of pairs, each of which is labelled as being from the same person or from different persons- the learning process determines the range of variations for given persons and between different persons. In the second paradigm the identity of a person is learnt when presented with multiple known samples of that person- where the variation and similarities within a particular person are learnt. The person-specific learning strategy is seen to perform better than general learning (5% higher performance with signatures). Improvement of person-specific performance with increasing number of samples is also observed. (Published abstract provided)