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Neural class-specific regression for face verification

Neural class-specific regression for face verification

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Face verification is a problem approached in the literature mainly using non-linear class-specific subspace learning techniques. While it has been shown that kernel-based class-specific discriminant analysis is able to provide excellent performance in small- and medium-scale face verification problems, its application in today's large-scale problems is difficult due to its training space and computational requirements. In this study, generalising on kernel-based class-specific discriminant analysis, it is shown that class-specific subspace learning can be cast as a regression problem. This allows them to derive linear, (reduced) kernel and neural network-based class-specific discriminant analysis methods using efficient batch and/or iterative training schemes, suited for large-scale learning problems. The authors test the performance of these methods in two datasets describing medium- and large-scale face verification problems.

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