Orientation truncated centre learning for deep face recognition
Recently, centre loss that aiming to assist Softmax loss with the objectives of both inter-class dispension and intra-class compactness simultaneously, has achieved remarkable performance on convolutional neural network-based face recognition. However, its advantages highly rely on the centre feature assumption, which influences the capacity of the final obtained face features. Inspired by the centre loss approach, a novel Orientation Truncated Centre Learning is proposed, which takes advantage of an orientation truncated centre function to make the centre feature learning have more suitable orientation for deep face recognition. Three metrics are proposed to evaluate how discriminative are the distributions of the learned features for MNIST visualisation. Experimental results on several challenging benchmarks, including fine-grained labelled faces in the wild (FGLFW), labelled faces in the wild (LFW), YouTube faces (YTF), and benchmark of large-scale unconstrained face recognition (BLUFR), show that the proposed approach can easily generate more favourable results than several state-of-the-art competitors.
