Neural class‐specific regression for face verification
Guanqun Cao, Alexandros Iosifidis, Moncef Gabbouj · IET Biometrics · 2017
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.