  Nowadays, deep learning is the standard approach for a wide range of
problems, including biometrics, such as face recognition and speech
recognition, etc. Biometric problems often use deep learning models to extract
features from images, also known as embeddings. Moreover, the loss function
used during training strongly influences the quality of the generated
embeddings. In this work, a loss function based on the decidability index is
proposed to improve the quality of embeddings for the verification routine. Our
proposal, the D-loss, avoids some Triplet-based loss disadvantages such as the
use of hard samples and tricky parameter tuning, which can lead to slow
convergence. The proposed approach is compared against the Softmax
(cross-entropy), Triplets Soft-Hard, and the Multi Similarity losses in four
different benchmarks: MNIST, Fashion-MNIST, CIFAR10 and CASIA-IrisV4. The
achieved results show the efficacy of the proposal when compared to other
popular metrics in the literature. The D-loss computation, besides being
simple, non-parametric and easy to implement, favors both the inter-class and
intra-class scenarios.
