Abstract: A face recognition neural network generates small dimensional facial feature vectors for faces in received images. A multi-task learning classifier network receives generated vectors and outputs classifications for the corresponding images. The classifications are built by respective sub-networks each comprising classification layers arranged in levels. The layers in at least some levels receive as input intermediate outputs from an immediately upstream classification layer in the same sub-network and in each other sub-network. After training, a resultant facial feature vector corresponding to an input image is received from the neural network. Resultant classifiers for the input image are received from the classifier network. A database is searched for a subset of reference facial feature vectors having associated classification features matching the resultant classifiers.

Abstract: A face recognition neural network generates small dimensional facial feature vectors for faces in received images. A multi-task learning classifier network receives generated vectors and outputs classifications for the corresponding images. The classifications are built by respective sub-networks each comprising classification layers arranged in levels. The layers in at least some levels receive as input intermediate outputs from an immediately upstream classification layer in the same sub-network and in each other sub-network. After training, a resultant facial feature vector corresponding to an input image is received from the neural network. Resultant classifiers for the input image are received from the classifier network. A database is searched for a subset of reference facial feature vectors having associated classification features matching the resultant classifiers.