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Deep Convolutional Neural Networks (DCNNs) and their variants have beenwidely used in large scale face recognition(FR) recently. Existing methods haveachieved good performance on many FR benchmarks. However, most of them sufferfrom two major problems. First, these methods converge quite slowly since theyoptimize the loss functions in a high-dimensional and sparse Gaussian Sphere.Second, the high dimensionality of features, despite the powerful descriptiveability, brings difficulty to the optimization, which may lead to a sub-optimallocal optimum. To address these problems, we propose a simple yet efficienttraining mechanism called MultiFace, where we approximate the originalhigh-dimensional features by the ensemble of low-dimensional features. Theproposed mechanism is also generic and can be easily applied to many advancedFR models. Moreover, it brings the benefits of good interpretability to FRmodels via the clustering effect. In detail, the ensemble of theselow-dimensional features can capture complementary yet discriminativeinformation, which can increase the intra-class compactness and inter-classseparability. Experimental results show that the proposed mechanism canaccelerate 2-3 times with the softmax loss and 1.2-1.5 times with Arcface orCosface, while achieving state-of-the-art performances in several benchmarkdatasets. Especially, the significant improvements on large-scaledatasets(e.g., IJB and MageFace) demonstrate the flexibility of our newtraining mechanism.

MultiFace: A Generic Training Mechanism for Boosting Face Recognition Performance