Data Quality Dependent Decision Making in Pattern Classification

Sensory information acquired by pattern recognition systems is invariably subject to environmental and sensing conditions, which may change over time. This may have a significant negative impact on the performance of pattern recognition algorithms. In the past, these problems have been tackled by building in invariance to the various changes, by adaptation and by multiple expert systems. More recently, the possibility of enhancing the pattern classification system robustness by using auxiliary information has been explored. In particular, by measuring the extent of degradation, the resulting sensory data quality information can be used with advantage to combat the effect of the degradation phenomena. This can be achieved by using the auxiliary quality information as features in the fusion stage of a multiple classifier system which uses the discriminant function values from the first stage as inputs. Data quality can be measured directly from the sensory data. Different architectures have been suggested for decision making using quality information. Examples of these architectures are presented and their relative merits discussed. The problems and benefits associated with the use of auxiliary information in sensory data analysis are illustrated on the problem of personal identity verification in biometrics.