In this paper, a face recognition system based on the fusion of two well-known appearance-based algorithms, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), is proposed. Fusion is performed at the decision-level, that is, the outputs of the individual face recognition algorithms are combined. Two main benefits of such fusion are shown. First, the reduction of the dependence on the environmental conditions with respect to the best individual recogniser. Secondly, the overall performance improvement over the best individual recogniser. To this end, fusion is investigated under different environmental conditions, namely, “ideal” conditions, characterised by a very limited variability of environmental parameters, and “real” conditions with large variability of lighting and face expressions.
Adaptive Multibiometric Systems
ROLI, FABIO
2011-01-01
Abstract
In this paper, a face recognition system based on the fusion of two well-known appearance-based algorithms, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), is proposed. Fusion is performed at the decision-level, that is, the outputs of the individual face recognition algorithms are combined. Two main benefits of such fusion are shown. First, the reduction of the dependence on the environmental conditions with respect to the best individual recogniser. Secondly, the overall performance improvement over the best individual recogniser. To this end, fusion is investigated under different environmental conditions, namely, “ideal” conditions, characterised by a very limited variability of environmental parameters, and “real” conditions with large variability of lighting and face expressions.
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