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Predictive models for multibiometric systems
Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations
Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration
Are face and object recognition abilities independent? Although it is
commonly believed that they are, Gauthier et al.(2014) recently showed that
these abilities become more correlated as experience with nonface categories
increases. They argued that there is a single underlying visual ability, v,
that is expressed in performance with both face and nonface categories as
experience grows. Using the Cambridge Face Memory Test and the Vanderbilt
Expertise Test, they showed that the shared variance between Cambridge Face
Memory Test and Vanderbilt Expertise Test performance increases monotonically
as experience increases. Here, we address why a shared resource across
different visual domains does not lead to competition and to an inverse
correlation in abilities? We explain this conundrum using our
neurocomputational model of face and object processing (The Model, TM). Our
results show that, as in the behavioral data, the correlation between
subordinate level face and object recognition accuracy increases as experience
grows. We suggest that different domains do not compete for resources because
the relevant features are shared between faces and objects. The essential power
of experience is to generate a "spreading transform" for faces that generalizes
to objects that must be individuated. Interestingly, when the task of the
network is basic level categorization, no increase in the correlation between
domains is observed. Hence, our model predicts that it is the type of
experience that matters and that the source of the correlation is in the
fusiform face area, rather than in cortical areas that subserve basic level
categorization. This result is consistent with our previous modeling
elucidating why the FFA is recruited for novel domains of expertise (Tong et
al., 2008)
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