  One-class CNNs have shown promise in novelty detection. However, very less
work has been done on extending them to multiclass classification. The proposed
approach is a viable effort in this direction. It uses one-class CNNs i.e., it
trains one CNN per class, for multiclass classification. An ensemble of such
one-class CNNs is used for multiclass classification. The benefits of the
approach are generally better recognition accuracy while taking almost even
half or two-thirds of the training time of a conventional multi-class deep
network. The proposed approach has been applied successfully to face
recognition and object recognition tasks. For face recognition, a 1000 frame
RGB video, featuring many faces together, has been used for benchmarking of the
proposed approach. Its database is available on request via e-mail. For object
recognition, the Caltech-101 Image Database and 17Flowers Dataset have also
been used. The experimental results support the claims made.
