Arguing that the convergence is very slow and very often the training can get stuck in local minima when dealing with many classes having very few samples, the researchers propose to use Additive Margin Softmax (AM-Softmax) loss function alongside with a novel optimization method that they call Dynamic Weight Imprinting (DWI).

Dynamic Weight Imprinting

Since Stochastic Gradient Descent updates the network with mini-batches, in a two-shot case (like the one of ID-selfie matching), each weight vector will receive signals only twice per epoch. These sparse attraction signals make little difference to the classifier weights. To overcome this problem, they propose a new optimization method where the idea is to update the weights based on sample features and therefore avoid underfitting of the classifier weights and accelerate the convergence.

Compared with stochastic gradient descend and other gradient-based optimization methods, the proposed DWI only updates the weights based on genuine samples. It only updates the weights of classes that are present in the mini-batch, and it works well with extensive datasets where the weight matrix of all classes is too large to be loaded, and only a subset of weights can be sampled for training.

The researchers trained the popular Face-ResNet architecture using stochastic gradient descent and AM-Softmax loss. Then they fine-tune the model on the ID-selfie dataset by binding the proposed Dynamic Weight Imprinting optimization method with the Additive Margin Softmax. Finally, a pair of sibling networks is trained for learning domain-specific features of IDs and selfies sharing high-level parameters.

Results

The proposed ID-selfie matching method achieves excellent result obtaining true acceptance rate TAR to 97.51 ± 0.40%. The authors report that their approach using the MS-Celeb-1M dataset and the AM-Softmax loss function achieves 99.67% accuracy on the standard verification protocol of LFW and a Verification Rate (VR) of 99.60% at False Accept Rate (FAR) of 0.1% on the BLUFR protocol.

Comparison with other state-of-the-art

The approach was compared with other state-of-the-art general face matches since there are no existing public ID-selfie matching methods. The comparison with these methods is given concerning TAR — true accept rate and FAR — false accept rate and shown in the tables below.

Conclusion

The proposed DocFace+ method for ID-selfie matching shows the potential of transfer learning, especially in tasks where not enough data is available. The proposed method is achieving high accuracy in selfie to ID matching and has potential to be employed in identity verification systems. Additionally, the proposed novel optimization method — Dynamic Weight Imprinting shows improved convergence and better generalization performance and represents a significant contribution to the field of machine learning.